Smart Brush: A real time segmentation tool for 3D medical images

Parascandolo, Patrizia; Cesario, Lorenzo; Vosilla, Loris; Pitikakis, Marios; Viano, Gianni

doi:10.1109/ispa.2013.6703826

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Some slight differences are visible when it comes to bone Table 1 The average and standard deviation of the accuracy ACC, Dice coefficient and mean absolute distance MAD between automated segmentation and manual segmentations performed on 3D images in three groups: distal parts of radius and ulna, metacarpal bones and carpal bones. Table 2 is slightly better than those calculated for SmartBrush semi-automated tool described in [9] where it was close to 0.95. We shortened significantly the computation time as compared to our previous method described in [10].…”

Section: Resultsmentioning

confidence: 61%

“…These methods were however essentially based on manual outlining of wrist bones or joints borders in 3 T MR images of wrist. Other recently published studies [8,9] used semi-automatic methods for quantifying lesions. Those methods [8,9] require however substantial manual work related to extraction of bone regions from a MR image (later we compare our results with one of them).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast automated segmentation of wrist bones in magnetic resonance images

Włodarczyk

Wojciechowski

Czaplicka

et al. 2015

Computers in Biology and Medicine

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Section: Resultsmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Fast automated segmentation of wrist bones in magnetic resonance images

Włodarczyk

Wojciechowski

Czaplicka

et al. 2015

Computers in Biology and Medicine

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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%

“…Unfortunately, the high computational cost and low accuracy of the recognition step of the bone segmentation within the volumetric CT or MRI exam is currently a limiting factor for automated methods in the clinical environment. It takes a high performance workstation 20+ minutes to attempt to identify the 8 carpal bones [1, 10, 45, 29] while a human observer could do the same task in several seconds with a single click on each bone. Conversely, finding the exact boundaries (the delineation step) is very time consuming for a human observer, but a computer can do this much faster.…”

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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“…Therefore, the challenge is to design a fast, generic, and easy segmentation tool that allows for generating clinical segmentations as well as fast ground truth annotations, in both 2D and 3D medical images and all modalities. The most related 2D segmentation technique is a Smart Brush tool [7,8]. However, the drawback of these methods is that they do not control the boundary smoothness [9].…”

Section: Introductionmentioning

confidence: 99%

Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation

et al. 2017

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Segmentation is one of the most important parts of medical image analysis. Manual segmentation is very cumbersome, time-consuming, and prone to inter-observer variability. Fully automatic segmentation approaches require a large amount of labeled training data and may fail in difficult or abnormal cases. In this work, we propose a new method for 2D segmentation of individual slices and 3D interpolation of the segmented slices. The Smart Brush functionality quickly segments the region of interest in a few 2D slices. Given these annotated slices, our adapted formulation of Hermite radial basis functions reconstructs the 3D surface. Effective interactions with less number of equations accelerate the performance and, therefore, a real-time and an intuitive, interactive segmentation of 3D objects can be supported effectively. The proposed method is evaluated on 12 clinical 3D magnetic resonance imaging data sets and are compared to gold standard annotations of the left ventricle from a clinical expert. The automatic evaluation of the 2D Smart Brush resulted in an average Dice coefficient of 0.88 ± 0.09 for the individual slices. For the 3D interpolation using Hermite radial basis functions, an average Dice coefficient of 0.94 ± 0.02 is achieved.

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Smart Brush: A real time segmentation tool for 3D medical images

Cited by 12 publications

References 18 publications

Fast automated segmentation of wrist bones in magnetic resonance images

Fast automated segmentation of wrist bones in magnetic resonance images

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

Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation

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