An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model

Koh, Jaehan; Scott, Peter; Chaudhary, Vipin; Dhillon, Gurmeet

doi:10.1109/isbi.2011.5872677

Cited by 40 publications

(19 citation statements)

References 6 publications

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“…For example, in current methods that rely on deformable models (Koh et al, 2011; Mukherjee et al, 2010), the forces used in the algorithm are not immediately applicable for other datasets with different fields of view or MR-sequences. Redesigning such forces is time consuming and requires a certain level of expertise and familiarity with the algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…These approaches vary from the watershed based (Nieniewski and Serneels, 2002) to applications of deformable models (McIntosh and Hamarneh, 2006). To the best of our knowledge there are only three fully automatic methods in the literature for human MRI spinal cord segmentation (Koh et al, 2010, 2011; Mukherjee et al, 2010). Koh et al (2010) developed a gradient vector flow (Xu and Prince, 1998) magnitude approach as part of a computer-aided diagnosis (CAD) system.…”

Section: Introductionmentioning

confidence: 99%

“…Their algorithm estimates the spinal cord using the magnitude of the gradient vector flow edge map, followed by a connected component analysis to remove any holes in the segmentation. In Koh et al (2011), the same group developed a different approach to the problem using active contour models (Kass et al, 1988) based on saliency maps. Mukherjee et al (2010) also applied an active contour approach, but instead evolved an image gradient based, open-ended contour using dynamic programming-based energy-minimization.…”

Section: Introductionmentioning

confidence: 99%

“…The three existing methods mentioned above are designed to be used with T2 and T2*-weighted MRIs because it offers the best soft tissue contrast (Koh et al, 2010, 2011; Mukherjee et al, 2010). None of them provide an intuitive and easily generalized approach for addressing MR-sequences or fields of view outside of the particular dataset they were designed for.…”

Section: Introductionmentioning

confidence: 99%

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Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Chen

Carass

Oh³

et al. 2013

NeuroImage

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Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1-C5). The second consists of T1-weighted MRI that cover both the cervical and portions of the thoracic vertebrae (C1-T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Chen

Carass

Oh³

et al. 2013

NeuroImage

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show abstract

“…Other techniques used for segmentation are deformable contour methods [1,11,15]. These techniques use curves Fig.…”

Section: Introductionmentioning

confidence: 99%

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries

et al. 2015

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Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis-segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods.

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Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter

Dostál

Keřkovský

Koriťáková

et al. 2018

Magnetic Resonance Imaging

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An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model

Cited by 40 publications

References 6 publications

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries

Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter

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