A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation

• URL: http://arxiv.org/abs/2510.06276v1
• Date: Mon, 06 Oct 2025 18:07:17 GMT
• Title: A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
• Authors: Mehdi Rabiee, Sergio Greco, Reza Shahbazian, Irina Trubitsyna,
• Abstract summary: Focal Cortical Dysplasia (FCD) is a primary cause of drug-resistant epilepsy.<n>FCD is difficult to detect in brain magnetic resonance imaging (MRI) due to the subtle and small-scale nature of its lesions.<n>This paper presents a new framework for segmenting FCD regions in 3D brain MRI images.
• Score: 13.43616910092363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Focal Cortical Dysplasia (FCD) is a primary cause of drug-resistant epilepsy and is difficult to detect in brain {magnetic resonance imaging} (MRI) due to the subtle and small-scale nature of its lesions. Accurate segmentation of FCD regions in 3D multimodal brain MRI images is essential for effective surgical planning and treatment. However, this task remains highly challenging due to the limited availability of annotated FCD datasets, the extremely small size and weak contrast of FCD lesions, the complexity of handling 3D multimodal inputs, and the need for output smoothness and anatomical consistency, which is often not addressed by standard voxel-wise loss functions. This paper presents a new framework for segmenting FCD regions in 3D brain MRI images. We adopt state-of-the-art transformer-enhanced encoder-decoder architecture and introduce a novel loss function combining Dice loss with an anisotropic {Total Variation} (TV) term. This integration encourages spatial smoothness and reduces false positive clusters without relying on post-processing. The framework is evaluated on a public FCD dataset with 85 epilepsy patients and demonstrates superior segmentation accuracy and consistency compared to standard loss formulations. The model with the proposed TV loss shows an 11.9\% improvement on the Dice coefficient and 13.3\% higher precision over the baseline model. Moreover, the number of false positive clusters is reduced by 61.6%

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