TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Abstract

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.

arXiv commenthttps://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Cited by in corpus (3)

• A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial
• A Multimodal Deep Network for the Reconstruction of T2W MR Images
• Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury