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

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.

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Improving neural networks by preventing co-adaptation of feature detectors
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Brain Tumor Segmentation with Deep Neural Networks
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
• Deep Human Parsing with Active Template Regression
• Deep Neural Networks for Anatomical Brain Segmentation
• Robust Optimization for Deep Regression

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