用新损失函数提升癫痫脑部病变分割精度，减少误判
A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
- 结合Dice损失与各向异性总变差项，增强分割空间平滑性
- 在85例数据上提升11.9%的骰子系数，误报簇减少61.6%
- 适合需要高精度、低伪影的癫痫手术规划场景
局灶性皮质发育不良（FCD）是药物难治性癫痫的主要原因，其病灶在脑磁共振成像（MRI）中因细微且尺度小而难以识别。对3D多模态脑MRI图像中的FCD区域进行准确分割，对有效手术规划至关重要。然而，由于标注数据有限、病灶极小且对比度弱、3D多模态输入处理复杂，以及标准体素级损失函数难以保证输出平滑性和解剖一致性，该任务仍具挑战性。本文提出一种新的3D脑MRI图像FCD分割框架，采用先进的基于Transformer的编码器-解码器结构，并引入结合Dice损失与各向异性总变差（TV）项的新损失函数。该设计在不依赖后处理的前提下，增强了空间平滑性并减少了假阳性簇。在包含85名癫痫患者的公开FCD数据集上评估，所提方法显著优于标准损失配置：模型在骰子系数上提升11.9%，精确率提高13.3%，假阳性簇数量减少61.6%。
原文摘要 · Abstract (English)
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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