仅用强度信息实现子宫颈组织自动分割，精度高且无需复杂预处理。
Intensity-based Segmentation of Tissue Images Using a U-Net with a Pretrained ResNet-34 Encoder: Application to Mueller Microscopy
- 基于M11强度构建U-Net模型，采用预训练ResNet-34编码器
- 70张图像训练下像素准确率达89.71%，组织Dice系数达80.96%
- 适合小样本生物医学图像，可推广至多种组织和成像模态
薄组织切片的人工标注仍是穆勒显微镜中的耗时步骤，限制了其可扩展性。本文提出一种新型自动化方法，仅使用穆勒矩阵的总强度元素M11作为输入，结合预训练的ResNet-34编码器与U-Net结构，对小鼠子宫颈切片图像中的四类区域（背景、内口、宫颈组织、阴道壁）进行分割。在仅70张宫颈组织切片的训练数据下，模型在独立测试集上达到89.71%的像素准确率和80.96%的平均组织Dice系数。来自ImageNet的迁移学习使模型在典型小样本生物医学图像场景中仍保持高精度。该强度驱动框架所需预处理极少，易于拓展至其他成像模态和组织类型，并提供公开的图形化标注工具以支持实际部署。
原文摘要 · Abstract (English)
Manual annotation of the images of thin tissue sections remains a time-consuming step in Mueller microscopy and limits its scalability. We present a novel automated approach using only the total intensity M11 element of the Mueller matrix as an input to a U-Net architecture with a pretrained ResNet-34 encoder. The network was trained to distinguish four classes in the images of murine uterine cervix sections: background, internal os, cervical tissue, and vaginal wall. With only 70 cervical tissue sections, the model achieved 89.71% pixel accuracy and 80.96% mean tissue Dice coefficient on the held-out test dataset. Transfer learning from ImageNet enables accurate segmentation despite limited size of training dataset typical of specialized biomedical imaging. This intensity-based framework requires minimal preprocessing and is readily extensible to other imaging modalities and tissue types, with publicly available graphical annotation tools for practical deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。