Investigation of multi-cohort brain MRI segmentation in infants

Pengfei Fu; Yaping Wang; Jinyuan Shen; Xiujuan Geng

doi:10.1117/12.2613258

4 April 2022 Investigation of multi-cohort brain MRI segmentation in infants

Pengfei Fu, Yaping Wang, Jinyuan Shen, Xiujuan Geng

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323C (2022) https://doi.org/10.1117/12.2613258
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Accurate segmentation of infant brain magnetic resonance images is crucial for studying brain development. However, infant images even within a narrow age-range differ drastically in size and contrast. Here, we investigated whether deep-learning based methods evaluated in iSeg-2017 can be generalized to accurately segment brain data acquired from other cohorts with different acquisition and ages. ISeg-2017 and UNC infant datasets were used to investigate three methods: SemiDenseNet, HyperDenseNet, and 3D-DenseSeg. Results demonstrate that HyperDenseNet has better segmentation performance and generalizability. Moreover, we built a joint segmentation-registration method by applying HyperDenseNet for segmentation. Results show that the joint method produced better performance compared with only registration or segmentation.

Citation Download Citation

Pengfei Fu, Yaping Wang, Jinyuan Shen, and Xiujuan Geng "Investigation of multi-cohort brain MRI segmentation in infants", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323C (4 April 2022); https://doi.org/10.1117/12.2613258

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