MCNet: A multi-level context-aware network for the segmentation of adrenal gland in CT images.

Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, the small size of the adrenal gland, which occupies less than 1% of the abdominal CT slice, poses a significant challenge to accurate segmentation. To address this problem, we propose a novel multi-level context-aware network (MCNet) to segment adrenal glands in CT images. Our MCNet mainly consists of two components, i.e., the multi-level context aggregation (MCA) module and multi-level context guidance (MCG) module. Specifically, the MCA module employs multi-branch dilated convolutional layers to capture geometric information, which enables handling of changes in complex scenarios such as variations in the size and shape of objects. The MCG module, on the other hand, gathers valuable features from the shallow layer and leverages the complete utilization of feature information at different resolutions in various codec stages. Finally, we evaluate the performance of the MCNet on two CT datasets, including our clinical dataset (Ad-Seg) and a publicly available dataset known as Distorted Golden Standards (DGS), from different perspectives. Compared to ten other state-of-the-art segmentation methods, our MCNet achieves 71.34% and 75.29% of the best Dice similarity coefficient on the two datasets, respectively, which is at least 2.46% and 1.19% higher than other segmentation methods.

Exo-miR-1290-induced by COX-2 overexpression promotes cancer-associated fibroblasts activation and tumor progression by CUL3-Nrf2 pathway in lung adenocarcinoma.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are critically involved in tumor progression by maintaining extracellular mesenchyma (ECM) production and improving tumor development. Cyclooxygenase-2 (COX-2) has been proved to promote ECM formation and tumor progression. However, the mechanisms of COX-2 mediated CAFs activation have not yet been elucidated. Therefore, we conducted this study to identify the effects and mechanisms of COX-2 underlying CAFs activation by tumor-derived exosomal miRNAs in lung adenocarcinoma (LUAD) progression. METHODS: As measures of CAFs activation, the expressions of fibroblasts activated protein-1 (FAP-1) and α-smooth muscle actin (α-SMA), the main CAFs markers, were detected by Western blotting and Immunohistochemistry. And the expression of Fibronectin (FN1) was used to analyze ECM production by CAFs. The exosomes were extracted by ultracentrifugation and exo-miRNAs were detected by qRT-PCR. Herein, we further elucidated the implicated mechanisms using online prediction software, luciferase reporter assays, co-immunoprecipitation, and experimental animal models. RESULTS: In vivo, a positive correlation was observed between the COX-2 expression levels in parenchyma and α-SMA/FN1 expression levels in mesenchyma in LUAD. However, PGE2, one of major product of COX-2, did not affect CAFs activation directly. COX-2 overexpression increased exo-miR-1290 expression, which promoted CAFs activation. Furthermore, Cullin3 (CUL3), a potential target of miR-1290, was found to suppress COX-2/exo-miR-1290-mediated CAFs activation and ECM production, consequently impeding tumor progression. CUL3 is identified to induce the Nuclear Factor Erythroid 2-Related Factor 2 (NFE2L2, Nrf2) ubiquitination and degradation, while exo-miR-1290 can prevent Nrf2 ubiquitination and increase its protein stability by targeting CUL3. Additionally, we identified that Nrf2 is direcctly bound with promoters of FAP-1 and FN1, which enhanced CAFs activation by promoting FAP-1 and FN1 transcription. CONCLUSIONS: Our data identify a new CAFs activation mechanism by exosomes derived from cancer cells that overexpress COX-2. Specifically, COX-2/exo-miR-1290/CUL3 is suggested as a novel signaling pathway for mediating CAFs activation and tumor progression in LUAD. Consequently, this finding suggests a novel strategy for cancer treatment that may tackle tumor progression in the future. Video Abstract.

Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval.

Recent advances in 3-D sensors and 3-D modeling have led to the availability of massive amounts of 3-D data. It is too onerous and time consuming to manually label a plentiful of 3-D objects in real applications. In this article, we address this issue by transferring the knowledge from the existing labeled data (e.g., the annotated 2-D images or 3-D objects) to the unlabeled 3-D objects. Specifically, we propose a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR). It is mainly composed of three key modules: 1) siamese network-based visual feature learning; 2) mutual information (MI)-based feature enhancement; and 3) conditional domain classifier-based feature adaptation. First, we design a siamese network to encode both 3-D objects and 2-D images from two domains because of its balanced accuracy and efficiency. Besides, it can guarantee the same transformation applied to both domains, which is crucial for the positive domain shift. The core issue for the retrieval task is to improve the capability of feature abstraction, but the previous CD3DOR approaches merely focus on how to eliminate the domain shift. We solve this problem by maximizing the MI between the input 3-D object or 2-D image data and the high-level feature in the second module. To eliminate the domain shift, we design a conditional domain classifier, which can exploit multiplicative interactions between the features and predictive labels, to enforce the joint alignment in both feature level and category level. Consequently, the network can generate domain-invariant yet discriminative features for both domains, which is essential for CD3DOR. Extensive experiments on two protocols, including the cross-dataset 3-D object retrieval protocol (3-D to 3-D) on PSB/NTU, and the cross-modal 3-D object retrieval protocol (2-D to 3-D) on MI3DOR-2, demonstrate that the proposed DAGSN can significantly outperform state-of-the-art CD3DOR methods.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

CRDet: Improving Signet Ring Cell Detection by Reinforcing the Classification Branch.

Detecting signet ring cells on histopathologic images is a critical computer-aided diagnostic task that is highly relevant to cancer grading and patients' survival rates. However, the cells are densely distributed and exhibit diverse and complex visual patterns in the image, together with the commonly observed incomplete annotation issue, posing a significant barrier to accurate detection. In this article, we propose to mitigate the detection difficulty from a model reinforcement point of view. Specifically, we devise a Classification Reinforcement Detection Network (CRDet). It is featured by adding a dedicated Classification Reinforcement Branch (CRB) on top of the architecture of Cascade RCNN. The proposed CRB consists of a context pooling module to perform a more robust feature representation by fully making use of context information, and a feature enhancement classifier to generate a superior feature by leveraging the deconvolution and attention mechanism. With the enhanced feature, the small-sized cell can be better characterized and CRDet enjoys a more accurate signet ring cell identification. We validate our proposal on a large-scale real clinical signet ring cell data set. It is shown that CRDet outperforms several popular convolutional neural network-based object detection models on this particular task.