  Accurate segmentation of punctate white matter lesions (PWML) in preterm
neonates by an automatic algorithm can better assist doctors in diagnosis.
However, the existing algorithms have many limitations, such as low detection
accuracy and large resource consumption. In this paper, a novel spatiotemporal
transformation deep learning method called Trident Segmentation CNN (TS-CNN) is
proposed to segment PWML in MR images. It can convert spatial information into
temporal 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 whole
model is evaluated on a dataset of 704 MR images. Overall the method achieves
median 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. (The
code is now available on https://github.com/YalongLiu/Trident-Segmentation-CNN)
