Hierarchical Brain Parcellation with Uncertainty

Graham, Mark S.; Sudre, Carole H.; Varsavsky, Thomas; Tudosiu, Petru-Daniel; Nachev, Parashkev; Ourselin, Sébastien; Cardoso, M. Jorge

doi:10.1007/978-3-030-60365-6_3

Cited by 4 publications

(2 citation statements)

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“…Uncertainty quantification (UQ) can be used as a metric of reliability for DL approaches. UQ in DL has been investigated for a variety of medical imaging applications, such as physics-informed uncertainty-aware Brain MRI segmentation ( 25 ), modality synthesis ( 26 ), dMRI super resolution ( 27 ), brain parcellation ( 28 ), electrode bending prediction ( 29 , 30 ), and tumor segmentation ( 24 , 31 , 32 ).…”

Section: Introductionmentioning

confidence: 99%

Informative and Reliable Tract Segmentation for Preoperative Planning

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%

Informative and Reliable Tract Segmentation for Preoperative Planning

et al. 2022

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“…In addition to studying relevant work, Graham et al [27] proposed a novel brain parcellation scheme by generating a hierarchical classification tree which is adapted from a segmentation task and extending it for predicting decisions by modeling an uncertainty factor for each of them. However, none of the above approaches model the full 4D aspect of brain functional connectivity.…”

Section: Related Workmentioning

confidence: 99%

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

Kazemivash

2021

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ObjectiveBrain parcellation is an essential aspect of computational neuroimaging research and deals with segmenting the brain into (possibly overlapping) sub-regions employed to study brain anatomy or function. In the context of functional parcellation, brain organization which is often measured via temporal metrics such as coherence, is highly dynamic. This dynamic aspect is ignored in most research, which typically applies anatomically based, fixed regions for each individual, and can produce misleading results.MethodsIn this work, we propose a novel spatio-temporal-network (5D) brain parcellation scheme utilizing a deep residual network to predict the probability of each voxel belonging to a brain network at each point in time.ResultsWe trained 53 4D brain networks and evaluate the ability of these networks to capture spatial and temporal dynamics as well as to show sensitivity to individual or group-level variation (in our case with age).ConclusionThe proposed system generates informative spatio-temporal networks that vary not only across individuals but also over time and space.SignificanceThe dynamic 5D nature of the developed approach provides a powerful framework that expands on existing work and has potential to identify novel and typically ignored findings when studying the healthy and disordered brain.

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