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首页> 外文会议>Conference on Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues >Comparing Mask R-CNN and U-Net architectures for robust automatic segmentation of immune cells in immunofluorescence images of Lupus Nephritis biopsies
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Comparing Mask R-CNN and U-Net architectures for robust automatic segmentation of immune cells in immunofluorescence images of Lupus Nephritis biopsies

机译：比较掩盖R-CNN和U-NET架构，用于狼疮性肾炎活检免疫荧光图像中免疫细胞的鲁棒自动分割

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Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease with many clinical presentations including lupus nephritis (LuN), or chronic inflammation of the kidneys. Current therapies for SLE are only modestly effective, highlighting the need to better understand networks of immune cells in SLE and LuN. In this work, we assess the performance of two convolutional neural network (CNN) architectures -Mask R-CNN and U-Net- in the task of instance segmentation of 5 immune-cell classes in 31 LuN biopsies. Each biopsy was stained for myeloid dendritic cells (mDCs), plasmacytoid dendritic cells (pDCs), B cells, and two populations of T cells, then imaged on a Leica SP8 fluorescence confocal microscope. Two instances of Mask R-CNN were trained on manually segmented images-one on lymphocytes (T cells and B cells), and one on DCs (pDCs and mDCs)-resulting in an average network sensitivities of 0.88 ± 0.04 and 0.82 ± 0.03, respectively. Five U-Nets, one for each of the five individual cell classes, were trained resulting in an average sensitivity of 0.85 ± 0.09 across all cell classes. Mask R-CNN yielded fewer false positives for all cell classes, with an average precision of 0.76 ± 0.03 compared to the U-Net object-level average precision of 0.43 ± 0.12. Overall, Mask R-CNN was more robust than the U-Net for segmenting cells in immunofluorescence images of kidney biopsies from lupus nephritis patients.
机译：Systemic Lupus红斑（SLE）是一种复杂的全身自身免疫疾病，许多临床演示包括狼疮肾炎（LUN），或肾脏慢性炎症。 SLE的当前疗法仅谦虚有效，突出了更好地了解SLE和LUN中的免疫细胞网络。在这项工作中，我们评估了两个卷积神经网络（CNN）架构 - 扫描R-CNN和U-Net-在31例LUN活检中的5个免疫细胞类别的实例分割任务中的性能。将每个活组织检查染色用于骨髓树突细胞（MDC），血浆骨质树突细胞（PDC），B细胞和两个T细胞的两个群体，然后在Leica SP8荧光共聚焦显微镜上成像。在手动分段的图像上培训掩模R-CNN的两个实例 - 一对一的淋巴细胞（T细胞和B细胞），以及在DCS（PDC和MDC）上的一种，平均网络敏感性为0.88±0.04和0.82±0.03，分别。五个U-Net，一个用于五种单独的细胞类中的每一个，培训，在所有细胞类别上培训平均灵敏度为0.85±0.09。对于所有细胞类，掩模R-CNN产生了较少的误报，平均精度为0.76±0.03，而U-净物体电平平均精度为0.43±0.12。总体而言，掩模R-CNN比狼疮性肾炎患者肾活检的免疫荧光图像中的分段细胞的U-Net更稳健。

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