利用深度学习实现小儿溃疡性结肠炎组织病理图像中嗜酸性粒细胞计数的自动化

medRxiv - Gastroenterology Pub Date : 2024-04-05 DOI:10.1101/2024.04.03.24305251

James Reigle, Oscar Lopez-Nunez, Erik Drysdale, Dua Abuquteish, Xiaoxuan Liu, Juan Putra, Lauren Erdman, Anne M. Griffiths, Surya Prasath, Iram Siddiqui, Jasbir Dhaliwal

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引用次数: 0

摘要

背景从粘膜组织病理学图像中准确识别炎性细胞对诊断溃疡性结肠炎非常重要。结肠粘膜中嗜酸性粒细胞的鉴定与病程有关。细胞计数不仅耗时，而且会受到人为偏差的影响。在这项研究中，我们利用深度学习从粘膜组织病理学图像中开发了一种自动嗜酸性粒细胞计数工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images

Background Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning.

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medRxiv - Gastroenterology

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