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Abstract #1852

Improving Subcortical and Hippocampal Subfield Segmentation with a 3D Hybrid Deep Learning Solution

Aaron Cao1, Zongyu Li2, Jordan Jomsky2, Andrew Laine2, and Jia Guo2
1University of California, Santa Barbara, San Jose, CA, United States, 2Columbia University, New York City, NY, United States

Synopsis

Keywords: Analysis/Processing, Segmentation

Motivation: The most widely accepted brain segmentation tools like FreeSurfer are slow and inconvenient for large datasets. Faster deep-learning-based methods often sacrifice accuracy and reliability.

Goal(s): We propose a novel deep-learning architecture for subcortical and hippocampal subfield segmentation and achieve consistent state-of-the-art performance.

Approach: Our approach combines a 3D patch-based pipeline with a hybrid CNN-Mamba architecture, named MedSegMamba.

Results: We evaluated MedSegMamba on FreeSurfer ground truths across various T1w MRI datasets. For subcortical segmentation, MedSegMamba consistently demonstrates strong performance over leading deep-learning alternatives, including CNN-Mamba, CNN-Transformer, and pure CNN networks. For hippocampal subfield segmentation, only MedSegMamba learned to segment all regions.

Impact: Our proposed novel deep learning model, MedSegMamba, reliably demonstrated state-of-the-art segmentation performance and utility across numerous datasets. It outperformed other well-established deep learning tools on the difficult tasks of subcortical and hippocampal subfield segmentation. Code is available here: https://github.com/aaroncao06/MedSegMamba.

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Keywords