Authors :
Presenting Author: Anand Joshi, PhD – University of Southern California
Kenneth Taylor, PhD – Cleveland Clinic
Takfarinas Medani, PhD – University of Southern California
Chinmay Chinara, MS – University of Southern California
Dileep Nair, MD – Cleveland Clinic Foundation
Richard Leahy, PhD – University of Southern California
Rationale: Approximately one-third of epilepsy patients are resistant to pharmacotherapy despite the availability of over 20 anti-seizure drugs. If the epileptogenic zone (EZ) is localized, neurosurgery provides a viable therapy to treat these drug-resistant patients, with 40% to 70% becoming seizure-free. The success of the neurosurgery depends on an accurate identification of EZ. Retrospective studies linking presurgical features and resected brain areas can help develop markers that can guide the delineation of surgical resection. These studies require identification of the resection cavity (RC) on preoperative images allowing electrophysiological and imaging features to be correlated with surgical outcomes. RC segmentation is also crucial in neuro-oncology for estimating the gross tumor volume for radiotherapy.
Methods: We present a technique for automatically delineating surgical resection on pre-operative MRI using a U-Net neural network. As input, we assume T1-weighted pre- and post-op MRI images. As a preprocessing step, we remove non-brain tissue using our BrainSuite software. The pair of images are then input to a U-Net (Figure 1 a) that performs a non-linear registration of pre- and post-op MRIs. We used masked mean-squared error as the cost function, which allows for the alignment of non-resected brain regions. This is followed by error thresholding and connected component analysis.
For validation, we use a subset of the EPISURG dataset [1]. The dataset comprises 430 postoperative MRIs and RCs segmented by three human raters on partially overlapping subsets. Preoperative MRIs are present for 269 subjects, with 53 subjects having both pre- and post-op MRIs. The database also incorporates human-rater segmentations including 33 scans segmented by a researcher in neuroimaging, 34 scans segmented by a clinical research fellow, and 33 subjects segmented by a neurologist. All the scans were also automatically segmented using the proposed method. The automatic and manual delineations were compared using Dice coefficients.
Results: An example resection delineation identified using our technique is shown in Figure 1(b). For the 42 subjects delineated by a researcher in neuroimaging, the average Dice was 0.70. For the subjects delineated by a clinical research fellow, the average Dice was 0.79; for those delineated by a neurologist, the average Dice was 0.74. The histograms of Dice coefficients are shown in Figure 2.
Conclusions: Based on this initial validation, the higher Dice coefficient for the neurologist and clinical research fellow compared to the neuroimaging researcher is notable, indicating accurate delineations generated by the automated method. The software is documented and integrated into the BrainStorm and BrainSuite software packages. The code is available from
https://github.com/ajoshiusc/auto_resection_mask.
References:
[1] Pérez-García, F., et al. "EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quantitative analysis of resection neurosurgery for refractory epilepsy. University College London." DOI 1.0.5522 (2020): 04.Funding: This work is supported by NIH grants: R01EB026299, R01NS074980, and DoD grants: W81XWH181061, HT94252310149.