论文标题
从相邻的染色组织学习黑素细胞口罩
Learning Melanocytic Cell Masks from Adjacent Stained Tissue
论文作者
论文摘要
黑色素瘤是皮肤癌最具侵略性的形式之一，导致大部分皮肤癌死亡。但是，病理学家的黑色素瘤诊断表现出较低的界面可靠性。由于黑色素瘤是黑色素细胞的癌症，因此显然需要开发一种黑色素细胞分割工具，该工具对病理学家的可变性不可知并自动化像素级注释。然而，吉吉像素级病理学家的标签是不切实际的。在本文中，我们提出了一种训练深层神经网络，以从苏木精细胞和曙红（H＆E）染色切片和邻近组织截面的配对免疫组织化学（IHC）中分割黑色素细胞细胞，尽管具有不可思议的地面标记，但达到平均IOU的平均IOU。
Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates pixel-level annotation. Gigapixel-level pathologist labeling, however, is impractical. Herein, we propose a means to train deep neural networks for melanocytic cell segmentation from hematoxylin and eosin (H&E) stained sections and paired immunohistochemistry (IHC) of adjacent tissue sections, achieving a mean IOU of 0.64 despite imperfect ground-truth labels.
