Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference

Mehta, Raghav; Christinck, Thomas; Nair, T. Murlidharan; Bussy, Aurélie; Premasiri, Swapna; Costantino, Manuela; Chakravarty, M. Mallar; Arnold, Douglas L.; Gal, Yarin

doi:10.1109/tmi.2021.3114097

Cited by 16 publications

(9 citation statements)

References 37 publications

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“…One important conclusion is that since various annotators may segment the same object differently, an intuitive tool for editing the segmentation results is required. Moreover, visualization of the segmentation uncertainty can be utilized to improve the trust of annotators in the quality of the methods ( Nair et al, 2020 ; Mehta et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Gazit¹,

Faran²,

Stepovoy³

et al. 2022

Front. Hum. Neurosci.

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Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning.

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Section: Introductionmentioning

confidence: 99%

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Gazit¹,

Faran²,

Stepovoy³

et al. 2022

Front. Hum. Neurosci.

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“…Radiologists can avoid making measurements and decisions in areas with higher uncertainty scores and have more confidence in using DL‐generated images and quantitative maps. A recent study has demonstrated that by passing uncertainty information in concatenated tasks, the performance of the downstream task (e.g., segmentation or detection) can be improved 56 . The uncertainty maps generated by UP‐Net can potentially provide information and improve subsequent automatic liver MRI analysis, such as DL‐based liver segmentation and disease classification.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R₂* quantification using self‐gated stack‐of‐radial MRI

Shih

Kafalı

Calkins

et al. 2022

Magnetic Resonance in Med

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Purpose To develop a deep learning‐based method for rapid liver proton‐density fat fraction (PDFF) and R2* quantification with built‐in uncertainty estimation using self‐gated free‐breathing stack‐of‐radial MRI. Methods This work developed an uncertainty‐aware physics‐driven deep learning network (UP‐Net) to (1) suppress radial streaking artifacts because of undersampling after self‐gating, (2) calculate accurate quantitative maps, and (3) provide pixel‐wise uncertainty maps. UP‐Net incorporated a phase augmentation strategy, generative adversarial network architecture, and an MRI physics loss term based on a fat–water and R2* signal model. UP‐Net was trained and tested using free‐breathing multi‐echo stack‐of‐radial MRI data from 105 subjects. UP‐Net uncertainty scores were calibrated in a validation dataset and used to predict quantification errors for liver PDFF and R2* in a testing dataset. Results Compared with images reconstructed using compressed sensing (CS), UP‐Net achieved structural similarity index >0.87 and normalized root mean squared error <0.18. Compared with reference quantitative maps generated using CS and graph‐cut (GC) algorithms, UP‐Net achieved low mean differences (MD) for liver PDFF (−0.36%) and R2* (−0.37 s−1). Compared with breath‐holding Cartesian MRI results, UP‐Net achieved low MD for liver PDFF (0.53%) and R2* (6.75 s−1). UP‐Net uncertainty scores predicted absolute liver PDFF and R2* errors with low MD of 0.27% and 0.12 s−1 compared to CS + GC results. The computational time for UP‐Net was 79 ms/slice, whereas CS + GC required 3.2 min/slice. Conclusion UP‐Net rapidly calculates accurate liver PDFF and R2* maps from self‐gated free‐breathing stack‐of‐radial MRI. The pixel‐wise uncertainty maps from UP‐Net predict quantification errors in the liver.

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“…Finally, of the 21 papers that adopted an independent test set, only 3 of them (amounting to 6% of the 51 surveyed papers) reported confidence intervals or standard-error [13,14,15]. Note that 11 other papers used statistical testing to compare different approaches even though they did not provide confidence intervals [16,17,18,19,20,21,22,23,24,25,26].…”

Section: Discussionmentioning

confidence: 99%

How precise are performance estimates for typical medical image segmentation tasks?

Jurdi¹,

Colliot²

2022

Preprint

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An important issue in medical image processing is to be able to estimate not only the performances of algorithms but also the precision of the estimation of these performances. Reporting precision typically amounts to reporting standard-error of the mean (SEM) or equivalently confidence intervals. However, this is rarely done in medical image segmentation studies. In this paper, we aim to estimate what is the typical confidence that can be expected in such studies. To that end, we first perform experiments for Dice metric estimation using a standard deep learning model (U-net) and a classical task from the Medical Segmentation Decathlon. We extensively study precision estimation using both Gaussian assumption and bootstrapping (which does not require any assumption on the distribution). We then perform simulations for other test set sizes and performance spreads. Overall, our work shows that small test sets lead to wide confidence intervals (e.g. ∼8 points of Dice for 20 samples with σ 10).

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Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference

Cited by 16 publications

References 37 publications

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R₂* quantification using self‐gated stack‐of‐radial MRI

How precise are performance estimates for typical medical image segmentation tasks?

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Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference

Cited by 16 publications

References 37 publications

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R2* quantification using self‐gated stack‐of‐radial MRI

How precise are performance estimates for typical medical image segmentation tasks?

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Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R₂* quantification using self‐gated stack‐of‐radial MRI