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 two-year Engel outcomes data were available, 73% had Engel I 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, 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.
This paper advances a new paradigm of minimally invasive neurosurgical interventions through skull foramina, which promise to improve patient outcomes by reducing postoperative pain and recovery times, and perhaps even complication rates. The foramen ovale, a small opening in the base of the skull, is currently used to insert recording electrodes into the brain for diagnosing epilepsy and as a pathway for ablating the trigeminal nerve for facial pain. An MRI-compatible robotic platform to position neurosurgical tools along a prescribed trajectory through the foramen ovale can enable access to deep brain targets for diagnosis or intervention. In this paper, we describe design goals and constraints, determined both heuristically and empirically, for such a robotic system. These include the space available within the scanner around the patient, the set of possible needle angles of approach to the foramen ovale, patient positioning options within the scanner, and the force needed to tilt the needle to desired angles. These design considerations can be used to inform future work on the design of MRI-conditional robots to access the brain through the foramen ovale.
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