Robust deep learning from incomplete annotation for accurate lung nodule detection.

Deep learning plays a significant role in the detection of pulmonary nodules in low-dose computed tomography (LDCT) scans, contributing to the diagnosis and treatment of lung cancer. Nevertheless, its effectiveness often relies on the availability of extensive, meticulously annotated dataset. In this paper, we explore the utilization of an incompletely annotated dataset for pulmonary nodules detection and introduce the FULFIL (Forecasting Uncompleted Labels For Inexpensive Lung nodule detection) algorithm as an innovative approach. By instructing annotators to label only the nodules they are most confident about, without requiring complete coverage, we can substantially reduce annotation costs. Nevertheless, this approach results in an incompletely annotated dataset, which presents challenges when training deep learning models. Within the FULFIL algorithm, we employ Graph Convolution Network (GCN) to discover the relationships between annotated and unannotated nodules for self-adaptively completing the annotation. Meanwhile, a teacher-student framework is employed for self-adaptive learning using the completed annotation dataset. Furthermore, we have designed a Dual-Views loss to leverage different data perspectives, aiding the model in acquiring robust features and enhancing generalization. We carried out experiments using the LUng Nodule Analysis (LUNA) dataset, achieving a sensitivity of 0.574 at a False positives per scan (FPs/scan) of 0.125 with only 10% instance-level annotations for nodules. This performance outperformed comparative methods by 7.00%. Experimental comparisons were conducted to evaluate the performance of our model and human experts on test dataset. The results demonstrate that our model can achieve a comparable level of performance to that of human experts. The comprehensive experimental results demonstrate that FULFIL can effectively leverage an incomplete pulmonary nodule dataset to develop a robust deep learning model, making it a promising tool for assisting in lung nodule detection.

Auto-DenseUNet: Searchable neural network architecture for mass segmentation in 3D automated breast ultrasound.

Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) plays an important role in breast cancer analysis. Deep convolutional networks have become a promising approach in segmenting ABUS images. However, designing an effective network architecture is time-consuming, and highly relies on specialist's experience and prior knowledge. To address this issue, we introduce a searchable segmentation network (denoted as Auto-DenseUNet) based on the neural architecture search (NAS) to search the optimal architecture automatically for the ABUS mass segmentation task. Concretely, a novel search space is designed based on a densely connected structure to enhance the gradient and information flows throughout the network. Then, to encourage multiscale information fusion, a set of searchable multiscale aggregation nodes between the down-sampling and up-sampling parts of the network are further designed. Thus, all the operators within the dense connection structure or between any two aggregation nodes can be searched to find the optimal structure. Finally, a novel decoupled search training strategy during architecture search is also introduced to alleviate the memory limitation caused by continuous relaxation in NAS. The proposed Auto-DenseUNet method has been evaluated on our ABUS dataset with 170 volumes (from 107 patients), including 120 training volumes and 50 testing volumes split at patient level. Experimental results on testing volumes show that our searched architecture performed better than several human-designed segmentation models on the 3D ABUS mass segmentation task, indicating the effectiveness of our proposed method.

Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images.

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64.57%, Jaccard coefficient (JC) of 53.39%, recall (REC) of 64.43%, precision (PRE) of 74.51%, 95th Hausdorff distance (95HD) of 11.91 mm, and average surface distance (ASD) of 4.63 mm. Our code will be available online (https://github.com/zhouyuegithub/CMVNet).

Dilated densely connected U-Net with uncertainty focus loss for 3D ABUS mass segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) images plays an important role in qualitative and quantitative ABUS image analysis. Yet this task is challenging due to the low signal to noise ratio and serious artifacts in ABUS images, the large shape and size variation of breast masses, as well as the small training dataset compared with natural images. The purpose of this study is to address these difficulties by designing a dilated densely connected U-Net (D2U-Net) together with an uncertainty focus loss. METHODS: A lightweight yet effective densely connected segmentation network is constructed to extensively explore feature representations in the small ABUS dataset. In order to deal with the high variation in shape and size of breast masses, a set of hybrid dilated convolutions is integrated into the dense blocks of the D2U-Net. We further suggest an uncertainty focus loss to put more attention on unreliable network predictions, especially the ambiguous mass boundaries caused by low signal to noise ratio and artifacts. Our segmentation algorithm is evaluated on an ABUS dataset of 170 volumes from 107 patients. Ablation analysis and comparison with existing methods are conduct to verify the effectiveness of the proposed method. RESULTS: Experiment results demonstrate that the proposed algorithm outperforms existing methods on 3D ABUS mass segmentation tasks, with Dice similarity coefficient, Jaccard index and 95% Hausdorff distance of 69.02%, 56.61% and 4.92 mm, respectively. CONCLUSIONS: The proposed method is effective in segmenting breast masses on our small ABUS dataset, especially breast masses with large shape and size variations.

Epiphyseal enchondroma masking as osteoid osteoma: a case report.

BACKGROUND: Enchondromas originating in the epiphyses of long bones are rare and epiphyseal osteoid osteomas are also uncommon. Diagnosis can become elusive when enchondromas or osteoid osteomas occur in atypical locations and present with nonspecific clinical and imaging characteristics. CASE PRESENTATION: We report a case of epiphyseal enchondroma of the left proximal femur in a 15-year-old girl with a 2-month history of left lower extremity pain. Preoperative CT displayed thickened cortex in the anterior surface of the left proximal femur with specks of calcification and inhomogeneity of the adjacent bone marrow cavity. She was diagnosed with osteoid osteoma. Postoperative pathological examination of surgically excised specimens revealed a diagnosis of enchondromas. CONCLUSIONS: Our case highlights that enchondroma should be considered in lesions of the epiphysis.

Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation.

Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this article, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled images and a large number of unlabeled images. Considering the network output contains correct predictions and unreliable predictions, equally treating each prediction in pseudo label update and loss calculation may degrade the network performance. To alleviate this problem, the uncertainty map is estimated for each image. Then an adaptive ensembling momentum map and an uncertainty aware unsupervised loss are designed and integrated with TEs model. The effectiveness of the proposed UATE model is mainly verified on an ABUS dataset of 107 patients with 170 volumes, including 13382 2D labeled slices. The Jaccard index (JI), Dice similarity coefficient (DSC), pixel-wise accuracy (AC) and Hausdorff distance (HD) of the proposed method on testing set are 63.65%, 74.25%, 99.21% and 3.81mm respectively. Experimental results demonstrate that our semi-supervised method outperforms the fully supervised method, and get a promising result compared with existing semi-supervised methods.

Mechanistic insights into heavy metals affinity in magnetic MnO2@Fe3O4/poly(m-phenylenediamine) core-shell adsorbent.

Adsorption represents an attractive mean to remediate polluted water. Unfortunately, the surface positive charges, low surface area and complicated separation procedures inhibit the usability of poly (m-phenylenediamine) (PmPD) as an adsorbent for heavy metal removing. To overcome these drawbacks, a magnetic MnO2@Fe3O4/PmPD core-shell adsorbent was designed to remove heavy metals from water. The MnO2 shell, came from the redox reaction between KMnO4 and PmPD, increased the surface area and changed the surface electronegativity. MnO2@Fe3O4/PmPD could be easily separated from water. It showed a significant increase in heavy metals removal efficiency, with maximum capacities of 438.6 mg/g for Pb(II) and 121.5 mg/g for Cd(II), respectively. The affinity between heavy metals and MnO2@Fe3O4/PmPD were mainly due to electrostatic attraction, ion exchanges and coordinated interaction. Density functional theory (DFT) calculations further confirmed that Pb and Cd were bonded with O atoms. The calculated adsorption energy indicated that the (111) MnO2 facet presented stronger adsorption affinity toward Pb(II) than Cd(II). Additionally, FM150 (150 mg) could regenerate 22 L Pb(II) wastewater upon single passage through the filterable column with a flux of 20 mL/min. Thus, the present work demonstrates the promising potential of using MnO2@Fe3O4/PmPD for efficiently removing heavy metals from wastewater.

Accuracy of MRI diagnosis of early osteonecrosis of the femoral head: a meta-analysis and systematic review.

OBJECTIVE: To evaluate the overall diagnostic value related to magnetic resonance imaging (MRI) in patients with early osteonecrosis of the femoral head. METHODS: By searching multiple databases and sources, including PubMed, Cochrane, and Embase database, by the index words updated in December 2017, qualified studies were identified and relevant literature sources were also searched. The qualified studies included prospective cohort studies and cross-sectional studies. Heterogeneity of the included studies were reviewed to select proper effect model for pooled weighted sensitivity, specificity, and diagnostic odds ratio (DOR). Summary receiver operating characteristic (SROC) analyses were performed for meniscal tears. RESULTS: Forty-three studies related to diagnostic accuracy of MRI to detect early osteonecrosis of the femoral head were involved in the meta-analysis. The global sensitivity and specificity of MRI in early osteonecrosis of the femoral head were 93.0% (95% CI 92.0-94.0%) and 91.0% (95% CI 89.0%-93.0%), respectively. The global positive likelihood ratio and global negative likelihood ratio of MRI in early osteonecrosis of the femoral head were 2.74 (95% CI 1.98-3.79) and 0.18 (95% CI 0.14-0.23), respectively. The global DOR was 27.27 (95% CI 17.02-43.67), and the area under the SROC was 93.38% (95% CI 90.87%-95.89%). CONCLUSIONS: This review provides a systematic review and meta-analysis to evaluate the diagnostic accuracy of MRI in early osteonecrosis of the femoral head. Moderate to strong evidence indicated that MRI appears to be significantly associated with higher diagnostic accuracy for early osteonecrosis of the femoral head.