Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation

TL;DR

This paper enhances U-Net for histopathological image segmentation by adding multi-scale features and robust training strategies to handle noisy labels, resulting in improved accuracy on breast cancer lymph node images.

Contribution

It introduces explicit multi-scale feature integration and confidence-based auxiliary maps to improve segmentation accuracy under shape variability and noisy annotations.

Findings

Significant accuracy improvement over baseline U-Net.

Effective handling of noisy and incomplete annotations.

Enhanced segmentation of complex histology structures.

Abstract

Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work. Firstly, the accuracy of vanilla U-Net degrades when the target regions for segmentation exhibit significant variations in shape and size. Even though the U-Net already possesses some capability to analyze features at various scales, we propose to explicitly add multi-scale feature maps in each convolutional module of the U-Net encoder to improve segmentation of histology images. Secondly, the accuracy of a U-Net model also suffers when the annotations for supervised learning are noisy or incomplete. This can happen due to the inherent difficulty for a human expert to identify and delineate all instances of specific pathology very precisely and accurately. We address this challenge by introducing auxiliary confidence maps that emphasize less on the boundaries of the given target regions. Further, we utilize the bootstrapping properties of the deep network to address the missing annotation problem intelligently. In our experiments on a private dataset of breast cancer lymph nodes, where the primary task was to segment germinal centres and sinus histiocytosis, we observed substantial improvement over a U-Net baseline based on the two proposed augmentations.