MixNet: Multi-modality mix network for brain segmentation
Chen L, Merhof D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11383 LNCS
Pages Range: 367-377
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030117221
DOI: 10.1007/978-3-030-11723-8_37
Abstract
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D semantic-wise deep convolutional neural network to segment brain structure in multi-modality MRI images. The network is composed of our modified deep residual learning units. In the unit, we replace the traditional convolution layer with the dilated convolutional layer, which avoids the use of pooling layers and deconvolutional layers, reducing the number of network parameters. Final predictions are made by aggregating information from multiple scales and modalities. A pyramid pooling module is used to capture spatial information of the anatomical structures at the output end. In addition, we test three architectures (MixNetv1, MixNetv2 and MixNetv3) which fuse the modalities differently to see the effect on the results. Our network achieves the state-of-the-art performance. MixNetv2 was submitted to the MRBrainS challenge at MICCAI 2018 and won the 3rd place in the 3-label task. On the MRBrainS2018 dataset, which includes subjects with a variety of pathologies, the overall DSC (Dice Coefficient) of 84.7% (gray matter), 87.3% (white matter) and 83.4% (cerebrospinal fluid) were obtained with only 7 subjects as training data.
Involved external institutions
Germany (DE)How to cite
APA:
Chen, L., & Merhof, D. (2019). MixNet: Multi-modality mix network for brain segmentation. In Mauricio Reyes, Theo van Walsum, Alessandro Crimi, Farahani Keyvan, Spyridon Bakas, Hugo Kuijf (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 367-377). Granada, ESP: Springer Verlag.
MLA:
Chen, Long, and Dorit Merhof. "MixNet: Multi-modality mix network for brain segmentation." Proceedings of the 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018, Granada, ESP Ed. Mauricio Reyes, Theo van Walsum, Alessandro Crimi, Farahani Keyvan, Spyridon Bakas, Hugo Kuijf, Springer Verlag, 2019. 367-377.
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