cwmok / DIRAC

This is the official Pytorch implementation of "Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans" (MICCAI 2022), written by Tony C. W. Mok and Albert C. S. Chung.
MIT License
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alpha的取值问题 #8

Closed 18370029656 closed 1 year ago

18370029656 commented 1 year ago
image

请问在这一步中,alpha=0.015具体是怎么得到的?对于一个新的数据集,应该怎么计算对应的alpha值?期待您的回答

cwmok commented 1 year ago

Hi @18370029656,

We selected the alpha value based on the forward and backward error maps of the validation set, and we found it generalized well to the test set. An ideal alpha value should be able to highlight regions with absent correspondence, e.g., tumor and resected tumor region, while excluding the regions with valid correspondence. A more rigorous way to choose the alpha is to measure the overlap (e.g. Dice score) of thresholded region and the tumor region of the validation set.