Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound

TL;DR

This paper introduces Hough-CNN, a novel deep learning segmentation method using Hough voting and CNN features, demonstrating robustness and adaptability across MRI and ultrasound modalities for brain region segmentation.

Contribution

The paper presents a new CNN-based segmentation approach that incorporates Hough voting for automatic localization, applicable to multiple imaging modalities and regions.

Findings

Robust segmentation across MRI and ultrasound.

Effective with varying training data sizes.

Applicable to multiple brain regions.

Abstract

In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest. This approach does not only use the CNN classification outcomes, but it also implements voting by exploiting the features produced by the deepest portion of the network. We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities. In the attempt to show the capabilities and the behaviour of CNNs when they are applied to medical image analysis, we perform a systematic study of the performances of six different network architectures, conceived according to state-of-the-art criteria, in various situations. We evaluate the impact of both different amount of training data and different data dimensionality (2D, 2.5D and 3D) on the final results. We show results on both MRI and transcranial US volumes depicting respectively 26 regions of the basal ganglia and the midbrain.