Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning. Although supervised methods have achieved satisfactory performance in VS segmentation, they require full annotations by experts, which is laborious and time-consuming. In this work, we aim to tackle the VS and cochlea segmentation problem in an unsupervised domain adaptation setting. Our proposed method leverages both the image-level domain alignment to minimize the domain divergence and semi-supervised training to further boost the performance. Furthermore, we propose to fuse the labels predicted from multiple models via noisy label correction. Our results on the challenge validation leaderboard showed that our unsupervised method has achieved promising VS and cochlea segmentation performance with mean dice score of 0.8261 ± 0.0416; The mean dice value for the tumor is 0.8302 ± 0.0772. This is comparable to the weakly-supervised based method.
Selective amygdalohippocampectomy (SelAH) for mesial temporal lobe epilepsy (mTLE) involves the resection of the anterior hippocampus and the amygdala. A recent study related to SelAH reports that among 168 patients for whom twoyear Engel outcomes data were available, 73% had Engel 1 outcomes (free of disabling seizure); 16.6% had Engel II outcomes (rare disabling seizures); 4.7% had Engel III outcomes (worthwhile improvement); and 5.3% had Engel IV outcomes (no worthwhile improvement). Success rate among sites also varies greatly. Possible explanations for variability in outcomes are the resected volume and/or the subregion of the hippocampus and amygdala that have been resected. To explore this hypothesis, the accurate segmentation of the resected cavity needs to be performed on a large scale. This is, however, a difficult and time-consuming task that requires expertise. Here we explore using a nnUNET to perform the task. Inspired by Youngeun [15], a level set loss is used in addition to the original DICE and cross-entropy loss in nnUNET to capture the cavity boundaries better. We show that, even with a modest-sized training set (25 volumes), the median DICE value between automated and manual segmentations is 0.88, which suggests that the automatic and accurate segmentation of the resection cavity is achievable.
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