Accurate segmentation of the mid-sagittal corpus callosum as captured in magnetic resonance images is an important step in many clinical research studies for various neurological disorders. This task can be challenging, however, especially more so in clinical studies, like those acquired of multiple sclerosis patients, whose brain structures may have undergone significant changes, rendering accurate registrations and hence, (multi-) atlas-based segmentation algorithms inapplicable. Furthermore, the MRI scans to be segmented often vary significantly in terms of image quality, rendering many generic unsupervised segmentation methods insufficient, as demonstrated in a recent work. In this paper, we hypothesize that adopting a supervised approach to the segmentation task may bring a break-through to performance. By employing a discriminative learning framework, our method automatically learns a set of latent features useful for identifying the target structure that proved to generalize well across various datasets, as our experiments demonstrate. Our evaluations, as conducted on four large datasets collected from different sources, totaling 2,033 scans, demonstrates that our method achieves an average Dice similarity score of 0.93 on test sets, when the models were trained on at most 300 images, while the top-performing unsupervised method could only achieve an average Dice score of 0.77.
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