Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and t...
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Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and the peripheral zone. This work proposes and evaluates two models for 2D semantic segmentation of these two zones of the prostate. The first model (Model-A) uses an encoder-decoder architecture based on the global U-net and the local U-net architectures. The global U-net segments the whole prostate, whereas the local U-net segments the central gland. The peripheral zone is obtained by subtracting the central gland from the whole prostate. On the other hand, the second model (Model-B) uses an encoder-classifier architecture based on the VGG16 network. Model-B performs segmentation by classifying each pixel of a Magnetic Resonance Image (MRI) into three categories: background, central gland, and peripheral zone. Both models are tested using MRIs from the dataset NCI-ISBI 2013 Challenge. The experimental results show a superior segmentation performance for Model-A, encoder-decoder architecture, (DSC = 96.79% +/- 0.15% and IoU = 93.79% +/- 0.29%) compared to Model-B, encoder-classifier architecture, (DSC = 92.50% +/- 1.19% and IoU = 86.13% +/- 2.02%). Copyright (C) 2021 The Authors.
Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and t...
详细信息
Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and the peripheral zone. This work proposes and evaluates two models for 2D semantic segmentation of these two zones of the prostate. The first model (Model-A) uses an encoder-decoder architecture based on the global U-net and the local U-net architectures. The global U-net segments the whole prostate, whereas the local U-net segments the central gland. The peripheral zone is obtained by subtracting the central gland from the whole prostate. On the other hand, the second model (Model-B) uses an encoder-classifier architecture based on the VGG16 network. Model-B performs segmentation by classifying each pixel of a Magnetic Resonance Image (MRI) into three categories: background, central gland, and peripheral zone. Both models are tested using MRIs from the dataset NCI-ISBI 2013 Challenge. The experimental results show a superior segmentation performance for Model-A, encoder-decoder architecture, (DSC = 96.79% ± 0.15% and IoU = 93.79% ± 0.29%) compared to Model-B, encoder-classifier architecture, (DSC = 92.50%± 1.19% and IoU = 86.13% ±2.02%).
The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddres...
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The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.
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