Fast automated segmentation of wrist bones in magnetic resonance images

Włodarczyk, Justyna; Wojciechowski, Wadim; Czaplicka, Kamila; Urbanik, Andrzej; Tabor, Zbisław

doi:10.1016/j.compbiomed.2015.07.007

Cited by 16 publications

(12 citation statements)

References 14 publications

(30 reference statements)

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“…The first obstacle to the development of this technique is the fact that STIR sequence cannot be used to bone segmentation purposes, as clear boundaries are not visible between bones and some soft tissue structures, for instance the insertion of piriformis muscle to the sacrum. Preferred MRI sequence to the assessment of joints anatomical structure is T1-weighted sequence and it was used in previous studies to the automated segmentation of wrist bones [10,11]. The simplest solution of this problem may Fig.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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The aim of the study was to create the efficient tool for semi-automated detection of bone marrow oedema lesions in patients with axial spondyloarthritis (axSpA). MRI examinations of 22 sacroiliac joints of patients with confirmed axSpA-related sacroiliitis (median SPARCC score: 14 points) were included into the study. Design of our algorithm is based on Maksymowych et al. evaluation method and consists of the following steps: manual segmentation of bones (T1W sequence), automated detection of reference signal region, sacroiliac joint central lines and ROIs, a division of ROIs into quadrants, automated detection of inflammatory changes (STIR sequence). As a gold standard, two sets of manual lesion delineations were created. Two approaches to the performance assessment of lesion detection were considered: pixel-wise (detections compared pixel by pixel) and quadrant-wise (quadrant to quadrant). Statistical analysis was performed using Spearman's correlation coefficient. Correlation coefficient obtained for pixel-wise comparison of semi-automated and manual detections was 0.87 (p = 0.001), while for quadrant-wise analysis was 0.83 (p = 0.001). The correlation between two sets of manual detections was 0.91 for pixel-wise comparison (p = 0.001) and 0.88 (p = 0.001) for quadrant-wise approach. Spearman's correlation between two manual assessments was not statistically different from the correlation between semi-automated and manual evaluations, both for pixel-(p = 0.14) and quadrant-wise (p = 0.17) analysis. Average single slice processing time: 0.64 ± 0.30 s. Our method allows for objective detection of bone marrow oedema lesions in patients with axSpA. The quantification of affected pixels and quadrants has comparable reliability to manual assessment.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Expert observers reported spending 4 hours per MRI scan to manually segment the eight carpal bones. In comparison, other reported computational times for the MRI based carpal bone segmentation methods include 6.17 minutes [20], <9 minutes [46], and others simply reported as <30 minutes [10, 29, 45]. The methods from CT had computational times also quite long with <20 minutes [1] and <40 minutes [8].…”

Section: Discussionmentioning

confidence: 99%

“…However it suffers from motion (due to longer acquisition times), bias field, non-standardized image values, and partial voluming [44]. A recent method by Włodarczyk et al [46], an expansion on their earlier work [45], first found seed locations using the multi-Otsu thresholding algorithm on a user defined region of interest of the carpal bone area. These marker locations were then applied to a watershed algorithm for the final segmentation with a Dice overlap co-efficient of 89 ± 12% for the carpal bones.…”

Section: Discussionmentioning

confidence: 99%

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

Foster

Joshi

Borgese

et al. 2018

Computerized Medical Imaging and Graphics

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Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI. In our method, the boundary of the bones were iteratively found using prior known anatomical constraints and a shape-detection level set. The parameters of the method were optimized using a training dataset of 48 manually segmented carpal bones and evaluated on 112 carpal bones which included both healthy participants without known wrist conditions and participants with thumb basilar osteoarthritis (OA). Manual segmentation by two expert human observers was considered as a reference. On the healthy subject dataset we obtained a Dice overlap of 93.0 ± 3.8, Jaccard Index of 87.3 ± 6.2, and a Hausdorff distance of 2.7 ± 3.4 mm, while on the OA dataset we obtained a Dice overlap of 90.7 ± 8.6, Jaccard Index of 83.0 ± 10.6, and a Hausdorff distance of 4.0 ± 4.4 mm. The short computational time of 20.8 s per bone (or 5.1 s per bone in the parallelized version) and the high agreement with the expert observers gives WRIST the potential to be utilized in musculoskeletal research.

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“…Previous work on MRI wrist bone segmentation involved either (I) atlas- (7,11,12) or (II) seed-based (13)(14)(15) algorithms applied to T1-weighted spin echo sequences. Little work has been done on segmenting wrist bones directly from T2-weighted fat-suppressed images, which are the preferred images to depict BME (16).…”

Section: Original Articlementioning

confidence: 99%

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

Wong¹,

Shi²,

Fan³

et al. 2019

Quant. Imaging Med. Surg

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Background: Magnetic resonance imaging (MRI) allows accurate determination of soft tissue and bone inflammation in rheumatoid arthritis. Inflammation can be measured semi-quantitatively using the wellestablished RA-MRI scoring system (RAMRIS), but its application is time consuming in routine clinical practice. To fully realize an automated quantitation of inflammation scoring for clinical use, automatic segmentation of the wrist bones on MR imaging is needed. Most previous studies extracted the wrist bones on T1-weighted (T1W) MR images, and then used registration to segment T2W fat-suppressed images for bone marrow oedema quantification, introducing spatial errors into the process. Relatively little work has tried segmentation directly from T2W fat-suppressed images and no prior study have used convolution neural network (CNN) to segment the wrist bones. The purpose of this study is to develop a CNN-based algorithm for automated segmentation of the wrist bones in early rheumatoid arthritis (ERA) patients on T2W fat-saturated MR images. Methods: As preliminary tests indicated that out-of-the-box segmentation CNN U-net performance was compromised by close apposition of wrist tendons and bone, we separated the volumes prior to segmentation by using classification CNN Inception V3 to group images with similar features. The classified images were then segmented by individually trained U-net. We trained the networks on 40 cases and tested them on 11 cases derived from an MR imaging dataset of 51 patients with varying severity of ERA. Results: We obtained a wrist bone segmentation with an average dice similarity coefficient (DICE) of 0.888±0.014, when compared to a manually drawn label. These results are comparable to existing atlas-based methods. Conclusions: We have developed a fully automatic method to segment the wrist bones in ERA patients of varying severity directly from T2W fat-suppressed MR images. This compares well with manually drawn labels.

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Fast automated segmentation of wrist bones in magnetic resonance images

Cited by 16 publications

References 14 publications

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

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

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

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

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