Target-Specific Segmentation in Hematoxylin and Eosin (H&E) Stained Pathology Images Using Conditional Segmentation Networks

Abstract

Digital pathology analysis is an advancement in medical diagnostics that leverages digital imaging to enhance the examination and interpretation of pathology slides. By converting traditional glass slides into high-resolution digital images, digital pathology enables pathologists to review and analyze samples with greater precision and efficiency. Machine learning-based techniques in digital pathology are attracting significant attention from pathologists and biologists due to their ability to reduce analysis time and provide objective, accurate results. In particular, semantic segmentation approaches have been widely adopted to isolate specific proteins or cell areas within digital pathology images. However, these methods often exhibit biases toward particular datasets which leads to models that can only process specific targets and lack general applicability. To address this limitation, we propose a target-specific segmentation approach using conditional segmentation networks. We introduce a one-hot vector to control the isolation of target proteins in a conditional manner. The proposed method achieved a pixel accuracy of 83.6% and an intersection over union of 0.7954 on a public pathology segmentation dataset.