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Automatic segmentation of the Parkinson’s disease-related tissue, the substantia nigra (SN), is an important step towards the accurate computer-aided diagnosis systems. Recently deep learning methods have achieved the state-of-the-art performance of the automated segmentation in various scenarios of medical image analysis. However, to acquire high resolution segmentation results, the conventional deep learning frameworks depend heavily on the full size of annotated data, which is pretty time-consuming and expensive for the training of the model. Moreover, the SN structure is usually tiny and sensitive to the progression of Parkinson’s disease (PD), which brings more anatomic variations among cases. To deal with these problems, this paper combines the cascaded fully convolutional network (FCN) and the size-reweighted loss function to automatically segment the tiny subcortical tissue SN from T2 MRI volumes. Different from the conventional one-stage FCNs, we cascade two FCNs in a coarse-to-fine fashion for the high resolution segmentation of the SN. The first FCN is trained to locate the SN-contained ROI and produce a coarse segmentation mask from a down-sampled MRI volume. The second FCN solely segments the SN at full resolution based on the results of the first FCN. Additionally, by giving higher weights to the SN region, the size-reweighted loss function encourages the model to concentrate on the tiny SN structure. Our results showed that the proposed FCN achieves mean dice score of 68.92% in comparison with the baseline model 66.40%.
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