DoubleU-Net: Colorectal Cancer Diagnosis and Gland Instance Segmentation with Text-Guided Feature Control

Abstract

With the rapid therapeutic advancement in personalized medicine, the role of pathologists for colorectal cancer has greatly expanded from morphologists to clinical consultants. In addition to cancer diagnosis, pathologists are responsible for multiple assessments based on glandular morphology statistics, like selecting appropriate tissue sections for mutation analysis [ 6 ]. Therefore, we propose DoubleU-Net that determines the initial gland segmentation and diagnoses the histologic grades simultaneously, and then incorporates the diagnosis text data to produce more accurate final segmentation. Our DoubleU-Net shows three advantages: (1) Besides the initial segmentation, it offers histologic grade diagnosis and enhanced segmentation for full-scale assistance. (2) The textual features extracted from diagnosis data provide high-level guidance related to gland morphology, and boost the performance of challenging cases with seriously deformed glands. (3) It can be extended to segmentation tasks with text data like key clinical phrases or pathology descriptions. The model is evaluated on two public colon gland datasets and achieves state-of-the-art performance.