Autonomous labeling of surgical resection margins using a foundation model
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917108371161088 |
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| author | Yang, Xilin Aydin, Musa Lu, Yuhong Selcuk, Sahan Yoruc Bai, Bijie Zhang, Yijie Birkeland, Andrew Ehrlich, Katjana Bec, Julien Marcu, Laura Pillar, Nir Ozcan, Aydogan |
| author_facet | Yang, Xilin Aydin, Musa Lu, Yuhong Selcuk, Sahan Yoruc Bai, Bijie Zhang, Yijie Birkeland, Andrew Ehrlich, Katjana Bec, Julien Marcu, Laura Pillar, Nir Ozcan, Aydogan |
| contents | Assessing resection margins is central to pathological specimen evaluation and has profound implications for patient outcomes. Current practice employs physical inking, which is applied variably, and cautery artifacts can obscure the true margin on histological sections. We present a virtual inking network (VIN) that autonomously localizes the surgical cut surface on whole-slide images, reducing reliance on inks and standardizing margin-focused review. VIN uses a frozen foundation model as the feature extractor and a compact two-layer multilayer perceptron trained for patch-level classification of cautery-consistent features. The dataset comprised 120 hematoxylin and eosin (H&E) stained slides from 12 human tonsil tissue blocks, resulting in ~2 TB of uncompressed raw image data, where a board-certified pathologist provided boundary annotations. In blind testing with 20 slides from previously unseen blocks, VIN produced coherent margin overlays that qualitatively aligned with expert annotations across serial sections. Quantitatively, region-level accuracy was ~73.3% across the test set, with errors largely confined to limited areas that did not disrupt continuity of the whole-slide margin map. These results indicate that VIN captures cautery-related histomorphology and can provide a reproducible, ink-free margin delineation suitable for integration into routine digital pathology workflows and for downstream measurement of margin distances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous labeling of surgical resection margins using a foundation model Yang, Xilin Aydin, Musa Lu, Yuhong Selcuk, Sahan Yoruc Bai, Bijie Zhang, Yijie Birkeland, Andrew Ehrlich, Katjana Bec, Julien Marcu, Laura Pillar, Nir Ozcan, Aydogan Computer Vision and Pattern Recognition Machine Learning Medical Physics Assessing resection margins is central to pathological specimen evaluation and has profound implications for patient outcomes. Current practice employs physical inking, which is applied variably, and cautery artifacts can obscure the true margin on histological sections. We present a virtual inking network (VIN) that autonomously localizes the surgical cut surface on whole-slide images, reducing reliance on inks and standardizing margin-focused review. VIN uses a frozen foundation model as the feature extractor and a compact two-layer multilayer perceptron trained for patch-level classification of cautery-consistent features. The dataset comprised 120 hematoxylin and eosin (H&E) stained slides from 12 human tonsil tissue blocks, resulting in ~2 TB of uncompressed raw image data, where a board-certified pathologist provided boundary annotations. In blind testing with 20 slides from previously unseen blocks, VIN produced coherent margin overlays that qualitatively aligned with expert annotations across serial sections. Quantitatively, region-level accuracy was ~73.3% across the test set, with errors largely confined to limited areas that did not disrupt continuity of the whole-slide margin map. These results indicate that VIN captures cautery-related histomorphology and can provide a reproducible, ink-free margin delineation suitable for integration into routine digital pathology workflows and for downstream measurement of margin distances. |
| title | Autonomous labeling of surgical resection margins using a foundation model |
| topic | Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2511.22131 |