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Accurate segmentation of punctate white matter lesions (PWML) in pretermneonates by an automatic algorithm can better assist doctors in diagnosis.However, the existing algorithms have many limitations, such as low detectionaccuracy and large resource consumption. In this paper, a novel spatiotemporaltransformation deep learning method called Trident Segmentation CNN (TS-CNN) isproposed to segment PWML in MR images. It can convert spatial information intotemporal information, which reduces the consumption of computing resources.Furthermore, a new improved training loss called Self-balancing Focal Loss(SBFL) is proposed to balance the loss during the training process. The wholemodel is evaluated on a dataset of 704 MR images. Overall the method achievesmedian DSC, sensitivity, specificity, and Hausdorff distance of 0.6355, 0.7126,0.9998, and 24.5836 mm which outperforms the state-of-the-art algorithm. (Thecode is now available on https://github.com/YalongLiu/Trident-Segmentation-CNN)

Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates