Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation

• URL: http://arxiv.org/abs/2303.00795v2
• Date: Fri, 3 Mar 2023 15:47:01 GMT
• Title: Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation
• Authors: Sadhana Ravikumar, Ranjit Ittyerah, Sydney Lim, Long Xie, Sandhitsu Das, Pulkit Khandelwal, Laura E.M. Wisse, Madigan L. Bedard, John L. Robinson, Terry Schuck, Murray Grossman, John Q. Trojanowski, Edward B. Lee, M. Dylan Tisdall, Karthik Prabhakaran, John A. Detre, David J. Irwin, Winifred Trotman, Gabor Mizsei, Emilio Artacho-P\'erula, Maria Mercedes I\~niguez de Onzono Martin, Maria del Mar Arroyo Jim\'enez, Monica Mu\~noz, Francisco Javier Molina Romero, Maria del Pilar Marcos Rabal, Sandra Cebada-S\'anchez, Jos\'e Carlos Delgado Gonz\'alez, Carlos de la Rosa-Prieto, Marta C\'orcoles Parada, David A. Wolk, Ricardo Insausti, Paul A. Yushkevich
• Abstract summary: We propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.
• Score: 10.416464319867881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace's equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.

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