SoftCTM：通过软实例分割与细胞-组织相互作用考量实现细胞检测
SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction
AI总结：
本研究针对计算病理学中H&E全切片图像的细胞检测分类任务，提出结合软实例分割真值、细胞-组织相互作用输入与测试时增强的SoftCTM模型，在OCELOT 2023挑战赛中获总排名第三。
摘要：
在组织病理学H&E染色全切片图像中检测并分类细胞是计算病理学的核心任务，因其能为肿瘤微环境研究提供宝贵信息。本研究探究了真值格式对模型性能的影响，同时通过将组织分割预测结果作为细胞检测模型的输入，纳入细胞-组织相互作用的考量。研究发现，“软”概率图形式的实例分割真值可带来最优模型性能。结合细胞-组织相互作用与测试时增强，我们的Soft Cell-Tissue-Model（软细胞-组织模型，SoftCTM）在Overlapped Cell On Tissue（组织重叠细胞，OCELOT）测试集上取得了0.7172的平均F1分数，在OCELOT 2023挑战赛中获得总排名第三的成绩。本方法的源代码已公开于https://github.com/lely475/ocelot23algo。
英文摘要：
Detecting and classifying cells in histopathology H\&E stained whole-slide images is a core task in computational pathology, as it provides valuable insight into the tumor microenvironment. In this work we investigate the impact of ground truth formats on the models performance. Additionally, cell-tissue interactions are considered by providing tissue segmentation predictions as input to the cell detection model. We find that a "soft", probability-map instance segmentation ground truth leads to best model performance. Combined with cell-tissue interaction and test-time augmentation our Soft Cell-Tissue-Model (SoftCTM) achieves 0.7172 mean F1-Score on the Overlapped Cell On Tissue (OCELOT) test set, achieving the third best overall score in the OCELOT 2023 Challenge. The source code for our approach is made publicly available at https://github.com/lely475/ocelot23algo.
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