Sci-Fri AM: Imaging - 07: Semi-Automated Segmentation of Carotid Artery Lumen and Wall from Three-Dimensional Ultrasound Images Using Level Sets

2010 ◽  
Vol 37 (7Part3) ◽  
pp. 3903-3903 ◽  
Author(s):  
E Ukwatta ◽  
J Awad ◽  
AD Ward ◽  
A Krasinski ◽  
A Fenster
Keyword(s):  
Carotid Artery ◽  
Level Sets ◽  
Three Dimensional ◽  
Ultrasound Images ◽  
Automated Segmentation

Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images

2010 ◽  
Vol 37 (4) ◽  
pp. 1382-1391 ◽  
Author(s):  
Joseph Awad ◽  
Adam Krasinski ◽  
Grace Parraga ◽  
Aaron Fenster
Keyword(s):  
Carotid Artery ◽  
Texture Analysis ◽  
Three Dimensional ◽  
Ultrasound Images ◽  
Carotid Artery Atherosclerosis

Semi-automated segmentation of carotid artery total plaque volume from three dimensional ultrasound carotid imaging

2012 ◽  
Author(s):  
D. Buchanan ◽  
I. Gyacskov ◽  
E. Ukwatta ◽  
T. Lindenmaier ◽  
A. Fenster ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Three Dimensional ◽  
Automated Segmentation ◽  
Plaque Volume

scholarly journals Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

2019 ◽  
Vol 46 (7) ◽  
pp. 3180-3193 ◽  
Author(s):  
Ran Zhou ◽  
Aaron Fenster ◽  
Yujiao Xia ◽  
J. David Spence ◽  
Mingyue Ding
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Ultrasound Images

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

Robotic motion compensation for bone movement, using ultrasound images

2015 ◽  
Vol 42 (5) ◽  
pp. 466-474 ◽  
Author(s):  
P.M.B. Torres ◽  
P. J. S. Gonçalves ◽  
J.M.M. Martins
Keyword(s):  
Motion Compensation ◽  
Optical Measurement ◽  
Three Dimensional ◽  
Point Clouds ◽  
Robotic System ◽  
Ultrasound Images ◽  
Bone Drilling ◽  
Content Type ◽  
3D Point Clouds ◽  
Robotic Motion

Purpose – The purpose of this paper is to present a robotic motion compensation system, using ultrasound images, to assist orthopedic surgery. The robotic system can compensate for femur movements during bone drilling procedures. Although it may have other applications, the system was thought to be used in hip resurfacing (HR) prosthesis surgery to implant the initial guide tool. The system requires no fiducial markers implanted in the patient, by using only non-invasive ultrasound images. Design/methodology/approach – The femur location in the operating room is obtained by processing ultrasound (USA) and computer tomography (CT) images, obtained, respectively, in the intra-operative and pre-operative scenarios. During surgery, the bone position and orientation is obtained by registration of USA and CT three-dimensional (3D) point clouds, using an optical measurement system and also passive markers attached to the USA probe and to the drill. The system description, image processing, calibration procedures and results with simulated and real experiments are presented and described to illustrate the system in operation. Findings – The robotic system can compensate for femur movements, during bone drilling procedures. In most experiments, the update was always validated, with errors of 2 mm/4°. Originality/value – The navigation system is based entirely on the information extracted from images obtained from CT pre-operatively and USA intra-operatively. Contrary to current surgical systems, it does not use any type of implant in the bone to track the femur movements.

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