An Object Aware Hybrid U-Net for Breast Tumour Annotation

TL;DR

This paper introduces a hybrid deep learning model combining U-Net and Active Contours to efficiently generate polygonal tumor annotations in histopathological slides, mimicking pathologist annotations for improved CAD analysis.

Contribution

The work presents a novel hybrid model that integrates deep learning with classical segmentation to produce pathologist-like tumor annotations, reducing manual effort.

Findings

Hybrid model outperforms state-of-the-art segmentation methods.

Effective in generating polygonal tumor boundaries.

Demonstrates potential for clinical annotation automation.

Abstract

In the clinical settings, during digital examination of histopathological slides, the pathologist annotate the slides by marking the rough boundary around the suspected tumour region. The marking or annotation is generally represented as a polygonal boundary that covers the extent of the tumour in the slide. These polygonal markings are difficult to imitate through CAD techniques since the tumour regions are heterogeneous and hence segmenting them would require exhaustive pixel wise ground truth annotation. Therefore, for CAD analysis, the ground truths are generally annotated by pathologist explicitly for research purposes. However, this kind of annotation which is generally required for semantic or instance segmentation is time consuming and tedious. In this proposed work, therefore, we have tried to imitate pathologist like annotation by segmenting tumour extents by polygonal boundaries. For polygon like annotation or segmentation, we have used Active Contours whose vertices or snake points move towards the boundary of the object of interest to find the region of minimum energy. To penalize the Active Contour we used modified U-Net architecture for learning penalization values. The proposed hybrid deep learning model fuses the modern deep learning segmentation algorithm with traditional Active Contours segmentation technique. The model is tested against both state-of-the-art semantic segmentation and hybrid models for performance evaluation against contemporary work. The results obtained show that the pathologist like annotation could be achieved by developing such hybrid models that integrate the domain knowledge through classical segmentation methods like Active Contours and global knowledge through semantic segmentation deep learning models.