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1.
IEEE Trans Med Imaging ; PP2021 Mar 29.
Artigo em Inglês | MEDLINE | ID: mdl-33780335

RESUMO

Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled the spatial features with short fixed-range temporal information, or separately learned visual and long temporal cues. In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features. We establish a long-range memory bank to serve as a memory cell storing the rich supportive information. Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. In this regard, our TMRNet enables the current feature to view the long-range temporal dependency, as well as tolerate complex temporal extents. We have extensively validated our approach on two benchmark surgical video datasets, M2CAI challenge dataset and Cholec80 dataset. Experimental results demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 67.0% v.s. 78.9% Jaccard on Cholec80 dataset).

2.
IEEE Trans Med Imaging ; 40(5): 1363-1376, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33507867

RESUMO

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

3.
Med Phys ; 48(3): 1182-1196, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33341975

RESUMO

PURPOSE: Volumetric medical image registration has important clinical significance. Traditional registration methods may be time-consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning-based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end-to-end. METHODS: We propose an end-to-end joint affine and deformable network for three-dimensional (3D) medical image registration. The proposed network combines two deformation methods; the first one is for obtaining affine alignment and the second one is a deformable subnetwork for achieving the nonrigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. Moreover, an anatomical similarity loss is devised to weakly supervise the training of the whole registration network. Finally, the trained network can perform deformable registration in one forward pass. RESULTS: The efficacy of our network was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI. Experimental results demonstrate our network consistently outperformed several state-of-the-art methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). CONCLUSIONS: The proposed network provides accurate and robust volumetric registration without any pre-alignment requirement, which facilitates the end-to-end deformable registration.

4.
IEEE J Biomed Health Inform ; 24(10): 2806-2813, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32915751

RESUMO

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/diagnóstico por imagem , Infecções por Coronavirus/diagnóstico , Aprendizado Profundo , Pandemias , Pneumonia Viral/diagnóstico por imagem , Pneumonia Viral/diagnóstico , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Biologia Computacional , Sistemas Computacionais , Infecções por Coronavirus/classificação , Bases de Dados Factuais/estatística & dados numéricos , Humanos , Aprendizado de Máquina , Pandemias/classificação , Pneumonia Viral/classificação , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos
5.
IEEE Trans Med Imaging ; 39(11): 3429-3440, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32746096

RESUMO

Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data. We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets, i.e., skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.

6.
Int J Comput Assist Radiol Surg ; 15(9): 1573-1584, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32588246

RESUMO

PURPOSE: Automatic surgical workflow recognition in video is an essentially fundamental yet challenging problem for developing computer-assisted and robotic-assisted surgery. Existing approaches with deep learning have achieved remarkable performance on analysis of surgical videos, however, heavily relying on large-scale labelled datasets. Unfortunately, the annotation is not often available in abundance, because it requires the domain knowledge of surgeons. Even for experts, it is very tedious and time-consuming to do a sufficient amount of annotations. METHODS: In this paper, we propose a novel active learning method for cost-effective surgical video analysis. Specifically, we propose a non-local recurrent convolutional network, which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames. We then formulate an intra-clip dependency score to represent the overall dependency within this clip. By ranking scores among clips in unlabelled data pool, we select the clips with weak dependencies to annotate, which indicates the most informative ones to better benefit network training. RESULTS: We validate our approach on a large surgical video dataset (Cholec80) by performing surgical workflow recognition task. By using our LRTD based selection strategy, we can outperform other state-of-the-art active learning methods who only consider neighbor-frame information. Using only up to 50% of samples, our approach can exceed the performance of full-data training. CONCLUSION: By modeling the intra-clip dependency, our LRTD based strategy shows stronger capability to select informative video clips for annotation compared with other active learning methods, through the evaluation on a popular public surgical dataset. The results also show the promising potential of our framework for reducing annotation workload in the clinical practice.

7.
IEEE Trans Cybern ; 2020 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-32248143

RESUMO

Segmentation of colorectal cancerous regions from 3-D magnetic resonance (MR) images is a crucial procedure for radiotherapy. Automatic delineation from 3-D whole volumes is in urgent demand yet very challenging. Drawbacks of existing deep-learning-based methods for this task are two-fold: 1) extensive graphics processing unit (GPU) memory footprint of 3-D tensor limits the trainable volume size, shrinks effective receptive field, and therefore, degrades speed and segmentation performance and 2) in-region segmentation methods supported by region-of-interest (RoI) detection are either blind to global contexts, detail richness compromising, or too expensive for 3-D tasks. To tackle these drawbacks, we propose a novel encoder-decoder-based framework for 3-D whole volume segmentation, referred to as 3-D RoI-aware U-Net (3-D RU-Net). 3-D RU-Net fully utilizes the global contexts covering large effective receptive fields. Specifically, the proposed model consists of a global image encoder for global understanding-based RoI localization, and a local region decoder that operates on pyramid-shaped in-region global features, which is GPU memory efficient and thereby enables training and prediction with large 3-D whole volumes. To facilitate the global-to-local learning procedure and enhance contour detail richness, we designed a dice-based multitask hybrid loss function. The efficiency of the proposed framework enables an extensive model ensemble for further performance gain at acceptable extra computational costs. Over a dataset of 64 T2-weighted MR images, the experimental results of four-fold cross-validation show that our method achieved 75.5% dice similarity coefficient (DSC) in 0.61 s per volume on a GPU, which significantly outperforms competing methods in terms of accuracy and efficiency. The code is publicly available.

8.
IEEE Trans Med Imaging ; 39(9): 2713-2724, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32078543

RESUMO

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.

9.
IEEE Trans Med Imaging ; 39(7): 2494-2505, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32054572

RESUMO

Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain. Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning. We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images, and outperforms the state-of-the-art domain adaptation approaches by a large margin.

10.
IEEE Trans Med Imaging ; 39(7): 2415-2425, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32012001

RESUMO

Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches.

11.
Med Image Anal ; 59: 101572, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31639622

RESUMO

Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis as well as very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is typically well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simultaneously boost the performance of both tasks. Specifically, our proposed MTRCNet-CL model has an end-to-end architecture with two branches, which share earlier feature encoders to extract general visual features while holding respective higher layers targeting for specific tasks. Given that temporal information is crucial for phase recognition, long-short term memory (LSTM) is explored to model the sequential dependencies in the phase recognition branch. More importantly, a novel and effective correlation loss is designed to model the relatedness between tool presence and phase identification of each video frame, by minimizing the divergence of predictions from the two branches. Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other. Extensive experiments on a large surgical video dataset (Cholec80) demonstrate outstanding performance of our proposed method, consistently exceeding the state-of-the-art methods by a large margin, e.g., 89.1% v.s. 81.0% for the mAP in tool presence detection and 87.4% v.s. 84.5% for F1 score in phase recognition.


Assuntos
Colecistectomia , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Gravação em Vídeo , Conjuntos de Dados como Assunto , Humanos
12.
IEEE Trans Cybern ; 50(9): 3950-3962, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31484154

RESUMO

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pathological field yet as currently well-addressed tasks are only the tip of the iceberg. Whole slide image (WSI) classification is a quite challenging problem. First, the scarcity of annotations heavily impedes the pace of developing effective approaches. Pixelwise delineated annotations on WSIs are time consuming and tedious, which poses difficulties in building a large-scale training dataset. In addition, a variety of heterogeneous patterns of tumor existing in high magnification field are actually the major obstacle. Furthermore, a gigapixel scale WSI cannot be directly analyzed due to the immeasurable computational cost. How to design the weakly supervised learning methods to maximize the use of available WSI-level labels that can be readily obtained in clinical practice is quite appealing. To overcome these challenges, we present a weakly supervised approach in this article for fast and effective classification on the whole slide lung cancer images. Our method first takes advantage of a patch-based fully convolutional network (FCN) to retrieve discriminative blocks and provides representative deep features with high efficiency. Then, different context-aware block selection and feature aggregation strategies are explored to generate globally holistic WSI descriptor which is ultimately fed into a random forest (RF) classifier for the image-level prediction. To the best of our knowledge, this is the first study to exploit the potential of image-level labels along with some coarse annotations for weakly supervised learning. A large-scale lung cancer WSI dataset is constructed in this article for evaluation, which validates the effectiveness and feasibility of the proposed method. Extensive experiments demonstrate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin with an accuracy of 97.3%. In addition, our method also achieves the best performance on the public lung cancer WSIs dataset from The Cancer Genome Atlas (TCGA). We highlight that a small number of coarse annotations can contribute to further accuracy improvement. We believe that weakly supervised learning methods have great potential to assist pathologists in histology image diagnosis in the near future.

13.
IEEE Trans Med Imaging ; 39(3): 718-728, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-31403410

RESUMO

Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET.

14.
Radiology ; 291(3): 677-686, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30912722

RESUMO

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck radiation therapy is labor intensive and highly variable among radiation oncologists. Purpose To construct and validate an artificial intelligence (AI) contouring tool to automate primary gross tumor volume (GTV) contouring in patients with NPC. Materials and Methods In this retrospective study, MRI data sets covering the nasopharynx from 1021 patients (median age, 47 years; 751 male, 270 female) with NPC between September 2016 and September 2017 were collected and divided into training, validation, and testing cohorts of 715, 103, and 203 patients, respectively. GTV contours were delineated for 1021 patients and were defined by consensus of two experts. A three-dimensional convolutional neural network was applied to 818 training and validation MRI data sets to construct the AI tool, which was tested in 203 independent MRI data sets. Next, the AI tool was compared against eight qualified radiation oncologists in a multicenter evaluation by using a random sample of 20 test MRI examinations. The Wilcoxon matched-pairs signed rank test was used to compare the difference of Dice similarity coefficient (DSC) of pre- versus post-AI assistance. Results The AI-generated contours demonstrated a high level of accuracy when compared with ground truth contours at testing in 203 patients (DSC, 0.79; 2.0-mm difference in average surface distance). In multicenter evaluation, AI assistance improved contouring accuracy (five of eight oncologists had a higher median DSC after AI assistance; average median DSC, 0.74 vs 0.78; P < .001), reduced intra- and interobserver variation (by 36.4% and 54.5%, respectively), and reduced contouring time (by 39.4%). Conclusion The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Chang in this issue.


Assuntos
Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Carcinoma Nasofaríngeo/diagnóstico por imagem , Neoplasias Nasofaríngeas/diagnóstico por imagem , Adolescente , Adulto , Algoritmos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Nasofaringe/diagnóstico por imagem , Estudos Retrospectivos , Adulto Jovem
15.
J Magn Reson Imaging ; 50(4): 1144-1151, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-30924997

RESUMO

BACKGROUND: The usefulness of 3D deep learning-based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis. PURPOSE: To evaluate the efficacy of 3D deep convolutional neural network (CNN) for diagnosing breast cancer and localizing the lesions at dynamic contrast enhanced (DCE) MRI data in a weakly supervised manner. STUDY TYPE: Retrospective study. SUBJECTS: A total of 1537 female study cases (mean age 47.5 years ±11.8) were collected from March 2013 to December 2016. All the cases had labels of the pathology results as well as BI-RADS categories assessed by radiologists. FIELD STRENGTH/SEQUENCE: 1.5 T dynamic contrast-enhanced MRI. ASSESSMENT: Deep 3D densely connected networks were trained under image-level supervision to automatically classify the images and localize the lesions. The dataset was randomly divided into training (1073), validation (157), and testing (307) subsets. STATISTICAL TESTS: Accuracy, sensitivity, specificity, area under receiver operating characteristic curve (ROC), and the McNemar test for breast cancer classification. Dice similarity for breast cancer localization. RESULTS: The final algorithm performance for breast cancer diagnosis showed 83.7% (257 out of 307) accuracy (95% confidence interval [CI]: 79.1%, 87.4%), 90.8% (187 out of 206) sensitivity (95% CI: 80.6%, 94.1%), 69.3% (70 out of 101) specificity (95% CI: 59.7%, 77.5%), with the area under the curve ROC of 0.859. The weakly supervised cancer detection showed an overall Dice distance of 0.501 ± 0.274. DATA CONCLUSION: 3D CNNs demonstrated high accuracy for diagnosing breast cancer. The weakly supervised learning method showed promise for localizing lesions in volumetric radiology images with only image-level labels. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1144-1151.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imagem por Ressonância Magnética/métodos , Mama/diagnóstico por imagem , Meios de Contraste , Aprendizado Profundo , Feminino , Humanos , Aumento da Imagem/métodos , Pessoa de Meia-Idade , Redes Neurais de Computação , Estudos Retrospectivos , Sensibilidade e Especificidade
16.
IEEE Trans Med Imaging ; 38(8): 1948-1958, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-30624213

RESUMO

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists' workload and simultaneously reduce misdiagnosis rate. However, the automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method is corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumor localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.


Assuntos
Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Metástase Linfática/diagnóstico por imagem , Algoritmos , Histocitoquímica , Humanos , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Metástase Linfática/patologia
17.
Med Image Anal ; 52: 199-211, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30594772

RESUMO

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net+ for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.


Assuntos
Adenocarcinoma/patologia , Neoplasias Colorretais/patologia , Aprendizado Profundo , Técnicas Histológicas/métodos , Processamento de Imagem Assistida por Computador/métodos , Automação , Humanos , Software
18.
Immunol Lett ; 201: 59-69, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30471320

RESUMO

BACKGROUND AND PURPOSE: Polygonum orientale L. (family: Polygonaceae), named Hongcao in China, has effects of dispelling wind and dampness, promoting blood circulation, and relieving pain. Our group has already studied and confirmed that POEa and POEe (ethyl acetate and ethyl ether extract of P. orientale, respectively) had anti-inflammatory and analgesic effects in early research, which was mainly relevant to the existence of flavonoids. According to the clinical application of P. orientale in traditional Chinese medicine, it has long been used for rheumatic arthralgia and rheumatoid arthritis. Therefore, our group further explored whether flavonoids of P. orientale have anti-rheumatoid arthritis effect and how does they play this role. METHODS: Dried small pieces of the stems and leaves of P. orientale were decocted with water and partitioned successively to obtain POEa and POEe, respectively. The anti-rheumatoid arthritis effect of P. orientale was studied by using a Freund's complete adjuvant (FCA)-induced arthritis (AIA) in a rat model. The levels of PGE2, TNF-α, and IL-1ß in serum of AIA rats were detected by enzyme linked immunosorbent assay (ELISA) to explore its mechanisms. In addition, we computationally studied the relationships between the 15 chemical components of POEa and POEe, and the currently focused 9 target proteins of rheumatoid arthritis by molecular docking. RESULTS: Pharmacological experiments showed that POEa and POEe significantly ameliorate symptoms of rheumatoid arthritis via reducing paw swelling volume, arthritis score, and thymus and spleen indices, as well as increasing body weight in AIA rats. Simultaneously, the concentrations of PGE2, TNF-α, and IL-1ß were significantly decreased by POEa and POEe. Histopathology revealed noticeable reduction in bone and cartilage, synovial hyperplasia, inflammatory cell infiltration, cartilage surface erosion, and joint degeneration by POEa and POEe treatment. In addition, the molecular docking studies showed that docking scores of 14 chemical compositions (including 12 flavonoids and 2 phenolic acids) of POEa and POEe with anti-rheumatoid arthritis protein targets were better than the complexed ligands of the anti-rheumatoid arthritis protein targets. Among them, six flavonoids in POEa and POEe had more docking protein targets (n ≥ 3). Five anti-rheumatoid arthritis targets including high-temperature requirement A1 protease (HtrA1), janus kinase 1 (JAK1), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (i-NOS), and prostaglandin E2 (PGE2) had better docking score compared with the complexed ligands. Moreover, most of the chemical components in POEa and POEe showed strong interaction with HtrA1. CONCLUSIONS: The flavonoids of P. orientale have anti-rheumatoid arthritis effect. In addition, the molecular docking results indicate that quercetin, catechol, orientin, and other six flavonoids may be closely related to HtrA1, JAK1, COX-2, i-NOS, and PGE2 protein target receptors. It suggests that these chemical compositions form strong protein-ligand complexes with these protein targets, especially HtrA1 to exert anti-rheumatoid arthritis. Further experimental studies show that mechanisms of anti-rheumatoid arthritis effects may also be relevant to inhibit the levels of PGE2, TNF-α, and IL-1ß in serum. Therefore, our group can further explore the possible active ingredients and mechanisms of the anti-rheumatoid arthritis effects of flavonoids, and focus on the inhibition of the expression of inflammatory factors and the TGF-ß1/Smad signaling pathway associated with HtrA1 protein target receptors, which can provide a direction and powerful reference for the action mechanism and drug research of anti-rheumatoid arthritis of flavonoids in P. orientale.


Assuntos
Anti-Inflamatórios/uso terapêutico , Artrite Experimental/terapia , Artrite Reumatoide/terapia , Fitoterapia/métodos , Extratos Vegetais/uso terapêutico , Polygonum/imunologia , Animais , Modelos Animais de Doenças , Humanos , Mediadores da Inflamação/metabolismo , Masculino , Medicina Tradicional Chinesa , Simulação de Acoplamento Molecular , Ratos , Ratos Sprague-Dawley
19.
IEEE Trans Med Imaging ; 37(12): 2663-2674, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-29994201

RESUMO

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.


Assuntos
Tomografia Computadorizada de Feixe Cônico/métodos , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias Hepáticas/diagnóstico por imagem , Fígado/diagnóstico por imagem , Algoritmos , Humanos
20.
IEEE Trans Med Imaging ; 37(5): 1114-1126, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29727275

RESUMO

We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. We effectively train the SV-RCNet in an end-to-end manner so that the visual representations and sequential dynamics can be jointly optimized in the learning process. In order to produce more discriminative spatio-temporal features, we exploit a deep residual network (ResNet) and a long short term memory (LSTM) network, to extract visual features and temporal dependencies, respectively, and integrate them into the SV-RCNet. Moreover, based on the phase transition-sensitive predictions from the SV-RCNet, we propose a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video. Such a strategy further improves the consistency of results and largely boosts the recognition performance. Extensive experiments have been conducted with the MICCAI 2016 Modeling and Monitoring of Computer Assisted Interventions Workflow Challenge dataset and Cholec80 dataset to validate SV-RCNet. Our approach not only achieves superior performance on these two datasets but also outperforms the state-of-the-art methods by a significant margin.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Software , Cirurgia Vídeoassistida/classificação , Algoritmos , Bases de Dados Factuais , Humanos , Fluxo de Trabalho
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