Thalamus segmentation using convolutional neural networks

Shangxian L. Wang; Shuo Han; Aaron Carass; Jiachen Zhuo; Steven Roys; Rao P. Gullapalli; Li Jiang; Jerry L. Prince

doi:10.1117/12.2582276

15 February 2021 Thalamus segmentation using convolutional neural networks

Shangxian L. Wang, Shuo Han, Aaron Carass, Jiachen Zhuo, Steven Roys, Rao P. Gullapalli, Li Jiang, Jerry L. Prince

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159634 (2021) https://doi.org/10.1117/12.2582276
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Thalamus segmentation plays an important role in studies that are related to neural system diseases. Existing thalamus segmentation algorithms use traditional image processing techniques on magnetic resonance images (MRI), which suffer from accuracy and efficiency. In recent years, deep convolutional neural networks (CNN) have been able to outperform many conventional algorithms in medical imaging tasks. We propose segmenting the thalamus using a 3D CNN that takes an MPRAGE image and a set of feature images derived from a diffusion tensor image (DTI). Experimental results demonstrate that using CNNs to segment the thalamus can improve accuracy and efficiency on various datasets.

Conference Presentation

Citation Download Citation

Shangxian L. Wang, Shuo Han, Aaron Carass, Jiachen Zhuo, Steven Roys, Rao P. Gullapalli, Li Jiang, and Jerry L. Prince "Thalamus segmentation using convolutional neural networks", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159634 (15 February 2021); https://doi.org/10.1117/12.2582276

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