Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

TL;DR

This paper introduces a cascade network for 3D medical image segmentation that uses a detection module to focus on regions of interest, improving accuracy and efficiency in volumetric data analysis.

Contribution

It presents a novel segmentation-by-detection cascade network that enhances 3D medical image segmentation by focusing on regions of interest, reducing noise influence.

Findings

Outperforms standard U-Net in 3D ultrasound femoral head segmentation

Reduces noise impact in volumetric data

Improves segmentation accuracy and efficiency

Abstract

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net.