Autonomous labeling of surgical resection margins using a foundation model

TL;DR

This paper introduces a virtual inking network (VIN) that autonomously localizes surgical resection margins on histological slides, reducing reliance on physical inks and artifacts, thereby standardizing and improving margin assessment in pathology.

Contribution

The study presents a novel deep learning-based method using foundation models for automatic, ink-free margin detection in whole-slide images, enhancing reproducibility and workflow integration.

Findings

Achieved ~73.3% region-level accuracy on unseen slides.

Produced margin overlays that qualitatively aligned with expert annotations.

Demonstrated robustness to cautery artifacts in histological images.

Abstract

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.