Punctate White Matter Lesion Segmentation in Preterm Infants Powered by
  Counterfactually Generative Learning
Zehua Ren Yongheng Sun Miaomiao Wang Yuying Feng Xianjun Li Chao Jin Jian Yang Chunfeng Lian Fan Wang
Accurate segmentation of punctate white matter lesions (PWMLs) are
fundamental for the timely diagnosis and treatment of related developmental
disorders. Automated PWMLs segmentation from infant brain MR images is
challenging, considering that the lesions are typically small and low-contrast,
and the number of lesions may dramatically change across subjects. Existing
learning-based methods directly apply general network architectures to this
challenging task, which may fail to capture detailed positional information of
PWMLs, potentially leading to severe under-segmentations. In this paper, we
propose to leverage the idea of counterfactual reasoning coupled with the
auxiliary task of brain tissue segmentation to learn fine-grained positional
and morphological representations of PWMLs for accurate localization and
segmentation. A simple and easy-to-implement deep-learning framework (i.e.,
DeepPWML) is accordingly designed. It combines the lesion counterfactual map
with the tissue probability map to train a lightweight PWML segmentation
network, demonstrating state-of-the-art performance on a real-clinical dataset
of infant T1w MR images. The code is available at
\href{https://github.com/ladderlab-xjtu/DeepPWML}{https://github.com/ladderlab-xjtu/DeepPWML}.