  Accurate segmentation of punctate white matter lesion (PWML) in infantile
brains by an automatic algorithm can reduce the potential risk of postnatal
development. How to segment PWML effectively has become one of the active
topics in medical image segmentation in recent years. In this paper, we
construct an efficient two-stage PWML semantic segmentation network based on
the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN).
We propose a heuristic RPN (H-RPN) which can utilize surrounding information
around the PWMLs for heuristic segmentation. Also, we design a lightweight
segmentation network to segment the lesion in a fast way. Densely connected
conditional random field (DCRF) is used to optimize the segmentation results.
We only use T1w MRIs to segment PWMLs. The result shows that our model can well
segment the lesion of ordinary size or even pixel size. The Dice similarity
coefficient reaches 0.6616, the sensitivity is 0.7069, the specificity is
0.9997, and the Hausdorff distance is 52.9130. The proposed method outperforms
the state-of-the-art algorithm. (The code of this paper is available on
https://github.com/YalongLiu/Refined-Segmentation-R-CNN)
