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Lucena O, Lavrador JP, Irzan H, Semedo C, Borges P, Vergani F, Granados A, Sparks R, Ashkan K, Ourselin S. Assessing informative tract segmentation and nTMS for pre-operative planning. J Neurosci Methods 2023; 396:109933. [PMID: 37524245 PMCID: PMC10861808 DOI: 10.1016/j.jneumeth.2023.109933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/15/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Deep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively. NEW METHOD In this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas. RESULTS Our results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask. COMPARISON WITH EXISTING METHODS We compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks. CONCLUSIONS In this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Keyoumars Ashkan
- King's College London, London, UK; King's College Hospital Foundation Trust, London, UK
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Lucena O, Borges P, Cardoso J, Ashkan K, Sparks R, Ourselin S. Informative and Reliable Tract Segmentation for Preoperative Planning. Front Radiol 2022; 2:866974. [PMID: 37492653 PMCID: PMC10365092 DOI: 10.3389/fradi.2022.866974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 07/27/2023]
Abstract
Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.
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Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Pedro Borges
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- King's College Hospital Foundation Trust, London, United Kingdom
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Lucena O, Vos SB, Vakharia V, Duncan J, Ashkan K, Sparks R, Ourselin S. Enhancing the estimation of fiber orientation distributions using convolutional neural networks. Comput Biol Med 2021; 135:104643. [PMID: 34280774 DOI: 10.1016/j.compbiomed.2021.104643] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/07/2021] [Accepted: 07/07/2021] [Indexed: 11/17/2022]
Abstract
Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.
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Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Department of Computer Sciences, University College London, London, UK; Neuroradiological Academic Unit, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Vejay Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - John Duncan
- Department of Clinical and Experimental Epilepsy, University College London, UK; National Hospital for Neurology and Neurosurgery, Queen Square, UK
| | | | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
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Hazem SR, Awan M, Lavrador JP, Patel S, Wren HM, Lucena O, Semedo C, Irzan H, Melbourne A, Ourselin S, Shapey J, Kailaya-Vasan A, Gullan R, Ashkan K, Bhangoo R, Vergani F. Middle Frontal Gyrus and Area 55b: Perioperative Mapping and Language Outcomes. Front Neurol 2021; 12:646075. [PMID: 33776898 PMCID: PMC7988187 DOI: 10.3389/fneur.2021.646075] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 01/29/2021] [Indexed: 12/20/2022] Open
Abstract
Background: The simplistic approaches to language circuits are continuously challenged by new findings in brain structure and connectivity. The posterior middle frontal gyrus and area 55b (pFMG/area55b), in particular, has gained a renewed interest in the overall language network. Methods: This is a retrospective single-center cohort study of patients who have undergone awake craniotomy for tumor resection. Navigated transcranial magnetic simulation (nTMS), tractography, and intraoperative findings were correlated with language outcomes. Results: Sixty-five awake craniotomies were performed between 2012 and 2020, and 24 patients were included. nTMS elicited 42 positive responses, 76.2% in the inferior frontal gyrus (IFG), and hesitation was the most common error (71.4%). In the pMFG/area55b, there were seven positive errors (five hesitations and two phonemic errors). This area had the highest positive predictive value (43.0%), negative predictive value (98.3%), sensitivity (50.0%), and specificity (99.0%) among all the frontal gyri. Intraoperatively, there were 33 cortical positive responses—two (6.0%) in the superior frontal gyrus (SFG), 15 (45.5%) in the MFG, and 16 (48.5%) in the IFG. A total of 29 subcortical positive responses were elicited−21 in the deep IFG–MFG gyri and eight in the deep SFG–MFG gyri. The most common errors identified were speech arrest at the cortical level (20 responses−13 in the IFG and seven in the MFG) and anomia at the subcortical level (nine patients—eight in the deep IFG–MFG and one in the deep MFG–SFG). Moreover, 83.3% of patients had a transitory deterioration of language after surgery, mainly in the expressive component (p = 0.03). An increased number of gyri with intraoperative positive responses were related with better preoperative (p = 0.037) and worse postoperative (p = 0.029) outcomes. The involvement of the SFG–MFG subcortical area was related with worse language outcomes (p = 0.037). Positive nTMS mapping in the IFG was associated with a better preoperative language outcome (p = 0.017), relating to a better performance in the expressive component, while positive mapping in the MFG was related to a worse preoperative receptive component of language (p = 0.031). Conclusion: This case series suggests that the posterior middle frontal gyrus, including area 55b, is an important integration cortical hub for both dorsal and ventral streams of language.
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Affiliation(s)
- Sally Rosario Hazem
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Mariam Awan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Jose Pedro Lavrador
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Sabina Patel
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Hilary Margaret Wren
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carla Semedo
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hassna Irzan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Shapey
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ahilan Kailaya-Vasan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Richard Gullan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Ranjeev Bhangoo
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Francesco Vergani
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
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