Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio (September 2014). Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation. In: Medical Image Computing and Computer-Assisted Intervention — MICCAI 2014. Proceedings, Part I. Lecture Notes in Computer Science: Vol. 17 (pp. 714-721). Springer

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In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

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