Fully automated segmentation of wrist bones on T2-weighted fat-suppressed MR images in early rheumatoid arthritis

Wong, Lun M.; Shi, Lin; Fan, Xiaoxi; Griffith, James F.

doi:10.21037/qims.2019.04.03

Cited by 10 publications

(6 citation statements)

References 21 publications

(30 reference statements)

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“…(36) Lun et al developed a CNN-based segmentation method for the wrist using T2-weighted fat-suppressed MRI images for early RA detection. (37) Despite the advantages of our proposed CNN-based approach for the detection of early RA indications, our study has the following limitations. First, we only analyzed the texture of the second, third, and fourth distal metacarpal bones for RA classi cation of radiographs.…”

Section: Discussionmentioning

confidence: 95%

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Huang

Miao²,

Zheng³

et al. 2020

Preprint

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BackgroundRheumatoid arthritis (RA) is characterized by altered bone microarchitecture (radiographically referred to as ‘texture’) of periarticular regions. We hypothesize that deep learning models can quantify periarticular texture changes to aid in the classification of early RA.MethodsThe second, third, and fourth distal metacarpal areas from hand radiographs of 892 early RA and 1236 non-RA patients were segmented for the Deep Texture Encoding Network (Deep-TEN; texture-based) and residual network-50 (ResNet-50; texture and structure-based) models to predict the probability of RA. The performances were measured using the area under the curve of the receiver operating characteristics curve (AUROC). Multivariate logistic regression was used to estimate the odds ratio (OR) with 95% confidence intervals (CIs) for RA.ResultsThe AUROC for RA was 0.69 for the Deep-TEN and 0.73 for the ResNet-50 model. The positive predictive values of a high texture score to classify RA using the Deep-TEN and ResNet-50 models were 0.64 and 0.67, respectively. High mean texture scores were associated with age- and sex-adjusted ORs (95% CI) for RA of 3.42 (2.59–4.50) and 4.30 (3.26–5.69) using the Deep-TEN and ResNet-50 models, respectively. The moderate and high RA risk groups determined by the Deep-TEN model were associated with adjusted ORs (95% CIs) of 2.48 (1.78–3.47) and 4.39 (3.11–6.20) for RA, respectively, and those using the ResNet-50 model were 2.17 (1.55–3.04) and 6.91 (4.83–9.90), respectively.ConclusionFully automated quantitative assessment for periarticular texture by deep learning models can help the classification of early RA.

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Section: Discussionmentioning

confidence: 95%

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Huang

Miao²,

Zheng³

et al. 2020

Preprint

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“…Similarly, deep learning-based segmentation has been described for thigh muscles and wrist bones [55,56].…”

Section: Mr Neurographymentioning

confidence: 99%

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Fritz

2021

Skeletal Radiol

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Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

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“…Many researchers have studied image segmentation (19)(20)(21)(22). Some approaches identify lesions based on preset shape-information of the target area and use machine learning-based algorithms to detect lesion features (23).…”

Section: Original Articlementioning

confidence: 99%

An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

Ren¹,

Zhou²,

Liu³

et al. 2020

Quant Imaging Med Surg

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Background: Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation.Methods: A region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area preprojection method, and an optimized region growing method. The algorithm can quickly and accurately segment a whole lung nodule in a set of computed tomography (CT) images based on an initial manual point. Results:The average time taken for ReGANS to segment 1 pulmonary nodule was 0.83s, and the probability rand index (PRI), global consistency error (GCE), and variation of information (VoI) from a comparison between the algorithm and the radiologist's 2 manual results were 0.93, 0.06, and 0.3 for the boundary range (BR), and 0.86, 0.06, 0.3 for the precise range (PR). The number of images covered by one pulmonary nodule in a CT image set was also evaluated to compare the segmentation algorithm with the radiologist's results, with an error rate of 15%. At the same time, the results were verified in multiple data sets to validate the robustness.Conclusions: Compared with other algorithms, ReGANS can segment the lung nodule image region more quickly and more precisely. The experimental results show that ReGANS can assist medical imaging diagnosis and has good clinical application value. It also provides a faster and more convenient method for pre-data preparation of intelligent algorithms.

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Fully automated segmentation of wrist bones on T2-weighted fat-suppressed MR images in early rheumatoid arthritis

Cited by 10 publications

References 21 publications

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

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