2 March 2018 MRI tumor segmentation with densely connected 3D CNN
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Abstract
Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network (3D CNN) based model to automatically segment gliomas. The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients. In order to accurately classify each voxel, our model captures multiscale contextual information by extracting features from two scales of receptive fields. To fully exploit the tumor structure, we propose a novel architecture that hierarchically segments different lesion regions of the necrotic and non-enhancing tumor (NCR/NET), peritumoral edema (ED) and GD-enhancing tumor (ET). Additionally, we utilize densely connected convolutional blocks to further boost the performance. We train our model with a patch-wise training schema to mitigate the class imbalance problem. The proposed method is validated on the BraTS 2017 dataset1 and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor, tumor core and enhancing tumor, respectively. These results are comparable to the reported state-of-the-art results, and our method is better than existing 3D-based methods in terms of compactness, time and space efficiency.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lele Chen, Lele Chen, Yue Wu, Yue Wu, Adora M. DSouza, Adora M. DSouza, Anas Z. Abidin, Anas Z. Abidin, Axel Wismüller, Axel Wismüller, Chenliang Xu, Chenliang Xu, } "MRI tumor segmentation with densely connected 3D CNN", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741F (2 March 2018); doi: 10.1117/12.2293394; https://doi.org/10.1117/12.2293394
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