Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex

Tremblay, M.; Albu, Alexandra Branzan; Hébert, Luc J.; Laurendeau, Denis

doi:10.1109/cccrv.2004.1301456

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Also, Donzelli have tried to achieve by solving problems of diarthrodial joint contact by the finite-element method [5]. In the visualization method of the shoulder's structure, Tremblay et al, have proposed the automatic segmentation method of the shoulder complex using MRI images [6]. However, there is no visualization method of the rotator cuff that is most important indication to diagnose humeral articulation.…”

Section: Introductionmentioning

confidence: 98%

3-D Visualization of Rotator Cuff by Combining T1* and T2* MR Images

Yogo

Kobashi

Kondo

et al. 2006

2006 World Automation Congress

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Rotator cuff tears of the shoulder are caused by bruise or age-related deterioration. They have been diagnosed by image diagnosis using magnetic resonance imaging (MRI). MR images provide the anatomical structures such as the muscle, the ligament and the bone by high resolution. However, it takes a long time and requires much effort for physicians to diagnose several tens of MR images. Additionally, physicians may overlook the disease of the shoulder because the structure of the shoulder is very complicate. To resolve these problems, we propose a method that estimates the rotator cuff by combining TI weighted and T2 weighted MR images using fuzzy inference techniques and visualizes as 3-D image.

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Section: Introductionmentioning

confidence: 98%

3-D Visualization of Rotator Cuff by Combining T1* and T2* MR Images

Yogo

Kobashi

Kondo

et al. 2006

2006 World Automation Congress

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show abstract

“…Branzan-Albu et al [4] and Tremblay et al [24] describe a technique for segmenting shoulders from T1-weighted MRI images of healthy shoulders. Although this work is based on MRI data rather than CT data, it is relevant because it specifically targets the shoulder.…”

Section: Introductionmentioning

confidence: 99%

Combined surface and volume processing for fused joint segmentation

et al. 2009

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Comparative exploration of whole-body MR through locally rigid transforms

et al. 2013

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Purpose Whole-body MRI is seeing increasing use in the study and diagnosis of disease progression. In this, a central task is the visual assessment of the progressive changes that occur between two whole-body MRI datasets, taken at baseline and follow-up. Current radiological workflow for this consists in manual search of each organ of interest on both scans, usually on multiple data channels, for further visual comparison. Large size of datasets, significant posture differences, and changes in patient anatomy turn manual matching in an extremely labor-intensive task that requires from radiologists high concentration for long period of time. This strongly limits the productivity and increases risk of underdiagnosis. Materials and methods We present a novel approach to the comparative visual analysis of whole-body MRI follow-up data. Our method is based on interactive derivation of locally rigid transforms from a pre-computed whole-body deformable registration. Using this approach, baseline and follow-up slices can be interactively matched with a single mouse click in the anatomical region of interest. In addition to the synchronized side-by-side baseline and matched follow-up slices, we have integrated four techniques to further facilitate the visual comparison of the two datasets: the “deformation sphere”, the color fusion view, the magic lens, and a set of uncertainty iso-contours around the current region of interest. Results We have applied our method to the study of cancerous bone lesions over time in patients with Kahler’s disease. During these studies, the radiologist carefully visually examines a large number of anatomical sites for changes. Our interactive locally rigid matching approach was found helpful in localization of cancerous lesions and visual assessment of changes between different scans. Furthermore, each of the features integrated in our software was separately evaluated by the experts. Conclusion We demonstrated how our method significantly facilitates examination of whole-body MR datasets in follow-up studies by enabling the rapid interactive matching of regions of interest and by the explicit visualization of change.

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Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex

Abstract: Abstract

Cited by 5 publications

References 12 publications

3-D Visualization of Rotator Cuff by Combining T1* and T2* MR Images

3-D Visualization of Rotator Cuff by Combining T1* and T2* MR Images

Combined surface and volume processing for fused joint segmentation

Comparative exploration of whole-body MR through locally rigid transforms

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