Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation
This work addresses the specific issue of data imbalance in medical image segmentation for MS lesions, offering an incremental improvement over existing methods.
The paper tackles the problem of segmenting Multiple Sclerosis lesions, which is challenging due to extreme class imbalance, by using a soft ground-truth mask constructed via morphological dilation to include neighboring pixels with reduced confidence weights; this approach improved the average Dice similarity coefficient on the ISBI 2015 challenge dataset and outperformed a second independent expert.
This paper explores the use of a soft ground-truth mask ("soft mask'') to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training. Utilizing the anatomical knowledge that the lesion surrounding pixels may also include some lesion level information, we suggest to increase the data set of the lesion class with neighboring pixel data - with a reduced confidence weight. A soft mask is constructed by morphological dilation of the binary segmentation mask provided by a given expert, where expert-marked voxels receive label 1 and voxels of the dilated region are assigned a soft label. In the methodology proposed, the FCNN is trained using the soft mask. On the ISBI 2015 challenge dataset, this is shown to provide a better precision-recall tradeoff and to achieve a higher average Dice similarity coefficient. We also show that by using this soft mask scheme we can improve the network segmentation performance when compared to a second independent expert.