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

arXiv: https://arxiv.org/abs/1601.07014 · HTML: https://mlsanity.com/paper/1601.07014/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2016.

## Built on by (most cited first, 17 of 17)

- [Convolutional Hough Matching Networks](https://mlsanity.com/paper/2103.16831/) (2021)
- [From CNN to Transformer: A Review of Medical Image Segmentation Models](https://mlsanity.com/paper/2308.05305/) (2023)
- [Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence](https://mlsanity.com/paper/2109.05221/) (2021)
- [Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound](https://mlsanity.com/paper/2405.09959/) (2024)
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- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains](https://mlsanity.com/paper/2110.09473/) (2021)
- [Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation](https://mlsanity.com/paper/2108.03117/) (2021)
- [Exploration of Convolutional Neural Network Architectures for Large Region Map Automation](https://mlsanity.com/paper/2211.03854/) (2022)
- [A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation](https://mlsanity.com/paper/2502.05396/) (2025)
- [Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future](https://mlsanity.com/paper/2103.14969/) (2021)
- [Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound](https://mlsanity.com/paper/2107.13833/) (2021)
- [DAGrid: Directed Accumulator Grid](https://mlsanity.com/paper/2306.02589/) (2023)
- [Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)](https://mlsanity.com/paper/2405.15563/) (2024)

## Abstract (arXiv)

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.