Progressive Dual Priori Network for Generalized Breast Tumor Segmentation.

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast and irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different centers. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC and HD95 of PDPNet were improved at least by 5.13% and 7.58% respectively on multi-center test sets. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregular tumors. Thus integrating them in a unified framework improved the multi-center breast tumor segmentation performance. The source code and open data can be accessed at https://github.com/wangli100209/PDPNet.

Deep learning for intracranial aneurysm segmentation using CT angiography.

Objective.This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.Approach.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.MainResults.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Significance.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.

Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network.

Intracranial aneurysm (IA) is an enormous threat to human health, which often results in nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly used computed tomographic angiography (CTA) examinations remains laborious and time consuming, leading to error-prone results in clinical practice, especially for small targets. In this study, we propose a fully automatic deep-learning model for IA segmentation that can be applied to CTA images. Our model, called Global Localization-based IA Network (GLIA-Net), can incorporate the global localization prior and generates the fine-grain three-dimensional segmentation. GLIA-Net is trained and evaluated on a big internal dataset (1,338 scans from six institutions) and two external datasets. Evaluations show that our model exhibits good tolerance to different settings and achieves superior performance to other models. A clinical experiment further demonstrates the clinical utility of our technique, which helps radiologists in the diagnosis of IAs.

A comparative study on proteomics between LNCap and DU145 cells by quantitative detection and SELDI analysis.

The differences in intracellular and extracellular protein expressions between human prostate cancer lines LNCap and DU145 were examined. The proteins of the two cell lines were extracted and condensed by using protein extraction kits. And the intracellular and extracellular proteins were quantitatively detected on a micro-plate reader by using bicinchoninic acid (BCA) method. The proteins in cell culture fluid were qualitatively assayed by SELDI-TOF-MS. The results showed that the intracellular protein contents of LNCap cells were extremely higher than those of DU145 cells. After serum-free culture, both intracellular and extracellular protein contents of LNCap and DU145 were decreased to some extent. And the intracellular proteins were decreased by 5% in LNCap and by 36% in DU145 respectively, while the extracellular proteins were decreased by 89% in LNCap and 96% in DU145 respectively. SELDI assay revealed that there were 5 marker proteins in LNCap and 6 in DU145. Although both LNCap and DU145 cell lines originated from human prostate cancer, they had some differences in protein expression.

A Comparative Study on Proteomics between LNCap and DU145 Cells by Quantitative Detection and SELDI Analysis / 华中科技大学学报（医学）（英德文版）

The differences in intracellular and extracellular protein expressions between human prostate cancer fines LNCap and DU145 were examined. The proteins of the two cell lines were extracted and condensed by using protein extraction kits. And the intracellular and extracellular proteins were quantitatively detected on a micro-plate reader by using bicinchoninie acid (BCA) method. The proteins in cell culture fluid were qualitatively assayed by SELDI-TOF-MS. The results showed that the intracellular protein contents of LNCap cells were extremely higher than those of DU145 cells. After serum-free culture, both intracellular and extracellular protein contents of LNCap and DU145 were decreased to some extent. And the intracellular proteins were decreased by 5% in LNCap and by 36% in DU145 respectively, while the extracellular proteins were decreased by 89% in LNCap and 96% in DU145 respectively. SELDI assay revealed that there were 5 marker proteins in LNCap and 6 in DU145. Although both LNCap and DU145 cell lines originated from human prostate cancer, they had some differences in protein expression.