Automatic Segmentation of Adipose Tissue from Thigh Magnetic Resonance Images

Purushwalkam, Senthil; Li, Baihua; Meng, Qinggang; McPhee, Jamie S.

doi:10.1007/978-3-642-39094-4_51

Cited by 7 publications

(6 citation statements)

References 7 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Engstrom et al [21] used a statistical shape model constrained with probabilistic MR atlases to automatically segment quadratus lumborum. Segmentation of muscle versus fatty tissues has been also performed through possibilistic clus-tering [22], histogram-based thresholding followed by region growing [23] and active contours [24] techniques.…”

Section: Related Workmentioning

confidence: 99%

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Conze

Brochard

Burdin

et al. 2020

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Conze

Brochard

Burdin

et al. 2020

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

“…Engstrom et al [20] used a statistical shape model constrained with probabilistic MR atlases to automatically segment quadratus lumborum. Segmentation of muscle versus fatty tissues has been also performed through possibilistic clus-tering [21], histogram-based thresholding followed by region growing [22] and active contours [23] techniques.…”

Section: Related Workmentioning

confidence: 99%

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Conze¹,

Brochard²,

Burdin³

et al. 2019

Preprint

View full text Add to dashboard Cite

Fully-automated segmentation of pathological shoulder muscles in patients with musculo-skeletal diseases is a challenging task due to the huge variability in muscle shape, size, location, texture and injury. A reliable automatic segmentation method from magnetic resonance images could greatly help clinicians to diagnose pathologies, plan therapeutic interventions and predict interventional outcomes while eliminating time consuming manual segmentation. The purpose of this work is three-fold. First, we investigate the feasibility of automatic pathological shoulder muscle segmentation using deep learning techniques, given a very limited amount of available annotated pediatric data. Second, we address the learning transferability from healthy to pathological data by comparing different learning schemes in terms of model generalizability. Third, extended versions of deep convolutional encoder-decoder architectures using encoders pre-trained on non-medical data are proposed to improve the segmentation accuracy. Methodological aspects are evaluated in a leave-one-out fashion on a dataset of 24 shoulder examinations from patients with unilateral obstetrical brachial plexus palsy and focus on 4 rotator cuff muscles (deltoid, infraspinatus, supraspinatus and subscapularis). The most accurate segmentation model is partially pre-trained on the large-scale ImageNet dataset and jointly

show abstract

“…From 2002 until 2015, twelve automated segmentation attempts on the MRI thigh muscles have been recorded [12,13,14,15,16,17,18,19,20,21,22,23]), where automation process was done by incorporating either one or combination of these techniques: thresholding (intensity based or histogram modelling); classification (fuzzy c-means (FCM) being the popular approach or k-means); active contour; and/or region growing. These methods focusing on segmenting muscles, marrow, femur, subcutaneous adipose tissue and/or intermuscular adipose tissue as an individual component (or group), whereafter the implementation of suitable pre-processing algorithms (to remove/reduce noise or improve pixel's intensity), this individual component (or group) can be straightforwardly segmented.…”

Section: Related Workmentioning

confidence: 99%

“…and 2015 [23] -96.8%, by snake active contour). Considering the application (or combination of applications) of such basic techniques, the system's average processing time across all methods are also recorded to be improved over time (2009 [18] -52 sec per image, by FCM and active contour; 2013 [21] -about 5.21 sec per image, by region growing and 3D intensity map; and 2015 [22] -0.25 sec per image, by k -means clustering). Two major reasons that understandably contribute to the better mean average processing time are superior computer technology and hardware available on the market and the optimisation of algorithms and processing platforms from the developer.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images

Ahmad¹,

Goyal²,

McPhee³

et al. 2018

Preprint

View full text Add to dashboard Cite

This paper presents an end-to-end solution for MRI thigh quadriceps segmentation. This is the first attempt that deep learning methods are used for the MRI thigh segmentation task. We use the state-of-the-art Fully Convolutional Networks with transfer learning approach for the semantic segmentation of regions of interest in MRI thigh scans. To further improve the performance of the segmentation, we propose a post-processing technique using basic image processing methods. With our proposed method, we have established a new benchmark for MRI thigh quadriceps segmentation with mean Jaccard Similarity Index of 0.9502 and processing time of 0.117 second per image.

show abstract

Automatic Segmentation of Adipose Tissue from Thigh Magnetic Resonance Images

Cited by 7 publications

References 7 publications

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images

Contact Info

Product

Resources

About