Volume Preserving Brain Lesion Segmentation

Automatic brain lesion segmentation plays an important role in clinical diagnosis and treatment. Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation due to its accuracy and efficiency. CNNs are generally trained with loss functions that measure the segmentation accuracy, such as the cross entropy loss and Dice loss. However, lesion load is a crucial measurement for disease analysis, and these loss functions do not guarantee that the volume of lesions given by CNNs agrees with that of the gold standard. In this work, we seek to address this challenge and propose volume preserving brain lesion segmentation, where a volume constraint is imposed on network outputs during the training process. Specifically, we design a differentiable mapping that approximates the volume of lesions using the segmentation probabilities. This mapping is then integrated into the training loss so that the preservation of brain lesion volume is encouraged. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, and experimental results show that our method better preserves the volume of brain lesions and improves the segmentation accuracy.