Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification

TL;DR

This paper proposes a method combining object detection and semantic segmentation to improve breast tumor identification in ultrasound scans, aiming for more accurate instance segmentation of tumors.

Contribution

It introduces a novel approach that integrates object detection with semantic segmentation to enhance breast tumor instance segmentation from ultrasound images.

Findings

Effective tumor region extraction from noisy ultrasound scans

Improved accuracy in tumor segmentation and classification

Potential for better diagnostic support in breast cancer detection

Abstract

Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest ( i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector.