Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI
Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection ca...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158222002194 |
| _version_ | 1828353660707405824 |
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| author | T. Campbell Arnold Ramya Muthukrishnan Akash R. Pattnaik Nishant Sinha Adam Gibson Hannah Gonzalez Sandhitsu R. Das Brian Litt Dario J. Englot Victoria L. Morgan Kathryn A. Davis, MD Joel M. Stein |
| author_facet | T. Campbell Arnold Ramya Muthukrishnan Akash R. Pattnaik Nishant Sinha Adam Gibson Hannah Gonzalez Sandhitsu R. Das Brian Litt Dario J. Englot Victoria L. Morgan Kathryn A. Davis, MD Joel M. Stein |
| author_sort | T. Campbell Arnold |
| collection | DOAJ |
| description | Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84–0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling. |
| first_indexed | 2024-04-14T02:12:54Z |
| format | Article |
| id | doaj.art-2ca1b456000b4f209e12ee05ed7772f4 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-04-14T02:12:54Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-2ca1b456000b4f209e12ee05ed7772f42022-12-22T02:18:23ZengElsevierNeuroImage: Clinical2213-15822022-01-0136103154Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRIT. Campbell Arnold0Ramya Muthukrishnan1Akash R. Pattnaik2Nishant Sinha3Adam Gibson4Hannah Gonzalez5Sandhitsu R. Das6Brian Litt7Dario J. Englot8Victoria L. Morgan9Kathryn A. Davis, MD10Joel M. Stein11Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Corresponding author at: Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia PA 19104, USA.Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Computer Science, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USACenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USACenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USACenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USACenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USAAccurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84–0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.http://www.sciencedirect.com/science/article/pii/S2213158222002194Postoperative MRITemporal lobe epilepsyResection cavityAutomated segmentationConvolutional neural networkHippocampal remnant |
| spellingShingle | T. Campbell Arnold Ramya Muthukrishnan Akash R. Pattnaik Nishant Sinha Adam Gibson Hannah Gonzalez Sandhitsu R. Das Brian Litt Dario J. Englot Victoria L. Morgan Kathryn A. Davis, MD Joel M. Stein Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI NeuroImage: Clinical Postoperative MRI Temporal lobe epilepsy Resection cavity Automated segmentation Convolutional neural network Hippocampal remnant |
| title | Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI |
| title_full | Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI |
| title_fullStr | Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI |
| title_full_unstemmed | Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI |
| title_short | Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI |
| title_sort | deep learning based automated segmentation of resection cavities on postsurgical epilepsy mri |
| topic | Postoperative MRI Temporal lobe epilepsy Resection cavity Automated segmentation Convolutional neural network Hippocampal remnant |
| url | http://www.sciencedirect.com/science/article/pii/S2213158222002194 |
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