The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis

Kucybała, Iwona; Tabor, Zbisław; Polak, Jakub; Urbanik, Andrzej; Wojciechowski, Wadim

doi:10.1007/s00296-020-04511-w

Cited by 12 publications

(20 citation statements)

References 21 publications

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“…Several previous studies have also investigated the use of threshold-based methods for quantifying inflammation [ 23 , 24 ]. However, these studies relied on manual segmentation to identify an optimal threshold, whereas our data suggest that using manual segmentation as a “gold standard” is problematic and may lead to inconsistent interpretation especially in cases when inflammation is subtle or precise lesion boundary cannot be identified.…”

Section: Discussionmentioning

confidence: 99%

“…However, these studies relied on manual segmentation to identify an optimal threshold, whereas our data suggest that using manual segmentation as a “gold standard” is problematic and may lead to inconsistent interpretation especially in cases when inflammation is subtle or precise lesion boundary cannot be identified. To highlight this point, a recent study aiming to demonstrate the feasibility of fully-automated segmentation of BME [ 25 ] revised the threshold value developed in earlier work [ 23 ], finding an optimal threshold of 1 compared to 1.5 in the prior study. Clearly, a threshold which depends on reference standard provided by human observers is not desirable.…”

Section: Discussionmentioning

confidence: 99%

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Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation

et al. 2023

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Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this ‘human-machine cooperation’ workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then ‘cleans’ the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation

et al. 2023

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“…For example, recall and precision in the HEALTHY population were low because of low pretest probability or alternatively because of potential dissimilar aspects of BME not caused by inflammation. Earlier studies that used more balanced or predominant positive data sets reported higher recall and accuracy, suggesting that performance improves with increasing pretest probability 11,12,14 . Translated to clinical practice, the most beneficial application of these prediction models would be in patients with high suspicion or known diagnosis of SpA.…”

Section: Discussionmentioning

confidence: 99%

“…Former studies elaborated on classification of SI joint MRI on a patient level (ie, binary outcome, sacroiliitis yes/no) or image level (ie, signs of sacroiliitis on individual MRI slices) but often required manual segmentation or annotation of the region of interest (ROI). Techniques offering a more detailed (ie, quadrant‐level) prediction of inflammatory lesions are scarce 14 . This work, therefore, aimed to develop and validate a fully automated ML algorithm to predict BME along the SI joints, suggestive of inflammation, on a quadrant level in patients with SpA and controls.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging

Roels

Craemer

Renson

et al. 2023

Arthritis & Rheumatology

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ObjectiveTo develop and validate a fully automated machine learning (ML) algorithm that predicts bone marrow oedema (BMO) on a quadrant‐level in sacroiliac (SI) joint MRI.MethodsA computer vision workflow automatically locates the SI joints, segments regions of interest (ilium and sacrum), performs objective quadrant extraction and predicts presence of BMO, suggestive of inflammatory lesions, on a quadrant‐level in semi‐coronal slices of T1/T2‐weighted MRI scans. Ground truth was determined by consensus among human readers. The inflammation classifier was trained using a ResNet18 backbone and 5‐fold cross‐validated on scans of spondyloarthritis (SpA) patients (n=279), postpartum (n=71), and healthy subjects (n=114); while independent SpA patient MRIs (n=243) served as test dataset. Patient‐level predictions were derived from aggregating quadrant‐level predictions, i.e. at least one positive quadrant.ResultsThe algorithm automatically detects the SI joints with a precision of 98.4% and segments ilium/sacrum with an intersection‐over‐union of 85.6% and 67.9%, respectively. The inflammation classifier performed well in cross‐validation: area under the curve (AUC) 94.5%, balanced accuracy (B‐ACC) 80.5%, and F1 score 64.1%. In the test dataset, AUC was 88.2%, B‐ACC 72.1%, and F1 score 50.8%. On a patient‐level, the model achieved a B‐ACC of 81.6% and 81.4% in the cross‐validation and test dataset, respectively.ConclusionWe propose a fully automated ML pipeline that enables objective and standardized evaluation of BMO along the SI joints on MRI. This method has the potential to screen large numbers of (suspected) SpA patients and is a step closer towards artificial intelligence assisted diagnosis and follow‐up.

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“…From the data set of 553 single-channel MRI slices 391 images were randomly assigned to a training subset while the remaining 162 images were assigned to the testing subset. Further details concerning the data set can be found in [10].…”

Section: A Materialsmentioning

confidence: 99%

Benchmarking of Deep Architectures for Segmentation of Medical Images

Gut

Tabor

Szymkowski

et al. 2022

IEEE Trans. Med. Imaging

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In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using identical conditions to disentangle the influence of model architecture, model training, and parameter settings on the performance of a trained model. For this purpose each of these six segmentation architectures is trained on the same nine data sets. The data sets are selected to cover various imaging modalities (X-rays, computed tomography, magnetic resonance imaging), single-and multi-class segmentation problems, and single-and multi-modal inputs. During the training, it is ensured that the data preprocessing, data set split into training, validation, and testing subsets, optimizer, learning rate change strategy, architecture depth, loss function, supervision and inference are exactly the same for all the architectures compared. Performance is evaluated in terms of Dice coefficient, surface Dice coefficient, average surface distance, Hausdorff distance, training, and prediction time. The main contribution of this experimental study is demonstrating that the architecture variants do not improve the quality of inference related to the basic U-Net architecture while resource demand rises.

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The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis

Cited by 12 publications

References 21 publications

Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation

Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation

Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging

Benchmarking of Deep Architectures for Segmentation of Medical Images

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