Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912178224758784 |
|---|---|
| author | Komura, Daisuke Takao, Maki Ochi, Mieko Onoyama, Takumi Katoh, Hiroto Abe, Hiroyuki Sano, Hiroyuki Konishi, Teppei Kumasaka, Toshio Yokose, Tomoyuki Miyagi, Yohei Ushiku, Tetsuo Ishikawa, Shumpei |
| author_facet | Komura, Daisuke Takao, Maki Ochi, Mieko Onoyama, Takumi Katoh, Hiroto Abe, Hiroyuki Sano, Hiroyuki Konishi, Teppei Kumasaka, Toshio Yokose, Tomoyuki Miyagi, Yohei Ushiku, Tetsuo Ishikawa, Shumpei |
| contents | The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response, yet current methods for its comprehensive analysis in H&E-stained tissue slides face significant limitations in the diversity of tissue cell types and accuracy. Here, we present PAGET (Pathological image segmentation via AGgrEgated Teachers), a new knowledge distillation approach that integrates multiple segmentation models while considering the hierarchical nature of cell types in the TME. By leveraging a unique dataset created through immunohistochemical restaining techniques and existing segmentation models, PAGET enables simultaneous identification and classification of 14 key TME components. We demonstrate PAGET's ability to perform rapid, comprehensive TME segmentation across various tissue types and medical institutions, advancing the quantitative analysis of tumor microenvironments. This method represents a significant step forward in enhancing our understanding of cancer biology and supporting precise clinical decision-making from large-scale histopathology images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis Komura, Daisuke Takao, Maki Ochi, Mieko Onoyama, Takumi Katoh, Hiroto Abe, Hiroyuki Sano, Hiroyuki Konishi, Teppei Kumasaka, Toshio Yokose, Tomoyuki Miyagi, Yohei Ushiku, Tetsuo Ishikawa, Shumpei Computer Vision and Pattern Recognition The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response, yet current methods for its comprehensive analysis in H&E-stained tissue slides face significant limitations in the diversity of tissue cell types and accuracy. Here, we present PAGET (Pathological image segmentation via AGgrEgated Teachers), a new knowledge distillation approach that integrates multiple segmentation models while considering the hierarchical nature of cell types in the TME. By leveraging a unique dataset created through immunohistochemical restaining techniques and existing segmentation models, PAGET enables simultaneous identification and classification of 14 key TME components. We demonstrate PAGET's ability to perform rapid, comprehensive TME segmentation across various tissue types and medical institutions, advancing the quantitative analysis of tumor microenvironments. This method represents a significant step forward in enhancing our understanding of cancer biology and supporting precise clinical decision-making from large-scale histopathology images. |
| title | Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.02909 |