1
|
Kiernan MJ, Al Mukaddim R, Mitchell CC, Maybock J, Wilbrand SM, Dempsey RJ, Varghese T. Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque. ULTRASONICS 2024; 137:107193. [PMID: 37952384 PMCID: PMC10841729 DOI: 10.1016/j.ultras.2023.107193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/19/2023] [Accepted: 10/29/2023] [Indexed: 11/14/2023]
Abstract
In patients at high risk for ischemic stroke, clinical carotid ultrasound is often used to grade stenosis, determine plaque burden and assess stroke risk. Analysis currently requires a trained sonographer to manually identify vessel and plaque regions, which is time and labor intensive. We present a method for automatically determining bounding boxes and lumen segmentation using a Mask R-CNN network trained on sonographer assisted ground-truth carotid lumen segmentations. Automatic lumen segmentation also lays the groundwork for developing methods for accurate plaque segmentation, and wall thickness measurements in cases with no plaque. Different training schemes are used to identify the Mask R-CNN model with the highest accuracy. Utilizing a single-channel B-mode training input, our model produces a mean bounding box intersection over union (IoU) of 0.81 and a mean lumen segmentation IoU of 0.75. However, we encountered errors in prediction when the jugular vein is the most prominently visualized vessel in the B-mode image. This was due to the fact that our dataset has limited instances of B-mode images with both the jugular vein and carotid artery where the vein is dominantly visualized. Additional training datasets are anticipated to mitigate this issue.
Collapse
Affiliation(s)
- Maxwell J Kiernan
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health (UW-SMPH), United States.
| | - Rashid Al Mukaddim
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health (UW-SMPH), United States
| | | | - Jenna Maybock
- Department of Neurological Surgery, UW-SMPH. Madison, WI, United States
| | | | - Robert J Dempsey
- Department of Neurological Surgery, UW-SMPH. Madison, WI, United States
| | - Tomy Varghese
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health (UW-SMPH), United States.
| |
Collapse
|
2
|
van Knippenberg L, van Sloun RJG, Mischi M, de Ruijter J, Lopata R, Bouwman RA. Unsupervised domain adaptation method for segmenting cross-sectional CCA images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107037. [PMID: 35907375 DOI: 10.1016/j.cmpb.2022.107037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data. METHODS In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs. RESULTS The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of-the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942). CONCLUSIONS The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.
Collapse
Affiliation(s)
- Luuk van Knippenberg
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands.
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands; Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands; Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands
| | - Joerik de Ruijter
- Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands
| | - Richard Lopata
- Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands; Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands
| |
Collapse
|
3
|
Al-Mohannadi A, Al-Maadeed S, Elharrouss O, Sadasivuni KK. Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement. SENSORS (BASEL, SWITZERLAND) 2021; 21:6839. [PMID: 34696054 PMCID: PMC8541435 DOI: 10.3390/s21206839] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/26/2021] [Accepted: 10/09/2021] [Indexed: 11/17/2022]
Abstract
Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.
Collapse
Affiliation(s)
- Aisha Al-Mohannadi
- Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar; (A.A.-M.); (O.E.)
| | - Somaya Al-Maadeed
- Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar; (A.A.-M.); (O.E.)
| | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar; (A.A.-M.); (O.E.)
| | | |
Collapse
|
4
|
Vila MDM, Remeseiro B, Grau M, Elosua R, Betriu À, Fernandez-Giraldez E, Igual L. Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation. Artif Intell Med 2020; 103:101784. [DOI: 10.1016/j.artmed.2019.101784] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 11/25/2019] [Accepted: 12/28/2019] [Indexed: 11/28/2022]
|
5
|
Biswas M, Kuppili V, Saba L, Edla DR, Suri HS, Sharma A, Cuadrado-Godia E, Laird JR, Nicolaides A, Suri JS. Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk. Med Biol Eng Comput 2018; 57:543-564. [PMID: 30255236 DOI: 10.1007/s11517-018-1897-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Accepted: 09/06/2018] [Indexed: 10/28/2022]
Abstract
Manual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature. Graphical abstract ᅟ.
Collapse
Affiliation(s)
- Mainak Biswas
- Department of Computer Science and Engineering, NIT Goa, Ponda, India
| | | | - Luca Saba
- Department of Radiology, A.O.U. Cagliari, Cagliari, Italy
| | | | - Harman S Suri
- Brown University, Providence, RI, USA.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Aditya Sharma
- Cardiovascular Division, University of Virginia, Charlottesville, VA, USA
| | - Elisa Cuadrado-Godia
- Dept. of Neurology, IMIM - Hospital del Mar, Passeig Marítim 25-29, Barcelona, Spain
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK.,Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
| |
Collapse
|
6
|
Kumar PK, Araki T, Rajan J, Laird JR, Nicolaides A, Suri JS. State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:155-168. [PMID: 30119850 DOI: 10.1016/j.cmpb.2018.05.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 04/29/2018] [Accepted: 05/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate, reliable, efficient, and precise measurements of the lumen geometry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter-adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements. METHODS The automated algorithms proposed for the segmentation of carotid lumen are broadly classified into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmentation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies. RESULTS Even though, we have found more boundary-based approaches compared to region-based approaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients. CONCLUSIONS We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial diameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information.
Collapse
Affiliation(s)
- P Krishna Kumar
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - John R Laird
- Heart and Vascular Institute, Adventist Health, St. Helena, CA, USA
| | | | - Jasjit S Suri
- Stroke Monitoring Division, AtheroPoint, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
| |
Collapse
|
7
|
Araki T, Kumar AM, Krishna Kumar P, Gupta A, Saba L, Rajan J, Lavra F, Sharma AM, Shafique S, Nicolaides A, Laird JR, Suri JS. Ultrasound-Based Automated Carotid Lumen Diameter/Stenosis Measurement and its Validation System. ACTA ACUST UNITED AC 2018. [DOI: 10.1177/154431671604000302] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Objective Degree of carotid stenosis is an important predictor to assess risk of stroke. Systolic velocity-based methods for lumen diameter and stenosis measurement are subjective. Image-based methods face a challenge because of low gradients in media and intima walls. Methods This article presents AtheroEdge™ 2.0, a two-stage process for automated carotid lumen diameter measurement that combats the above challenges. Stage one uses spectral analysis based on the hypothesis that far-wall adventitia is brightest. Stage two uses lumen pixel region identification based on the assumption that blood flow has constant density. Using global and local processing, lumen boundaries are detected. This clinical system outputs lumen diameter along with stenosis severity index (SSI). Results Our database consists of institutional review board–approved 202 patients (males/females: 155/47) left and right common carotid artery images (404 images, Toshiba scanner). Two trained neuro radiologists performed manual lumen border tracings using ImgTracer™ software. The coefficient of correlation between automated and two manual readings was 0.91 and 0.92. Dice similarity and Jaccard index were 95.82%, 95.72% and 92.10%, 91.92%, respectively. The mean diameter error between automated and two manual readings was 0.27 ± 0.26 and 0.26 ± 0.28 mm, respectively. Precision of merit was 98.05% and 99.03% with respect to two readings. SSI showed 97% accuracy. Conclusions The image-based automated carotid lumen diameter and stenosis measurement system is fast, accurate, and reliable.
Collapse
Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Asheed M. Kumar
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, India
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, California
| | - P. Krishna Kumar
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, India
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, California
| | - Ajay Gupta
- Radiology Department, Brain and Mind Research Institute, Weill Cornell Medical College, New York, New York
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, India
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, California
| | - Francesco Lavra
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Aditya M. Sharma
- Division of Cardiovascular Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | | | | | - John R. Laird
- UC Davis Vascular Center, University of California, Davis, California
| | - Jasjit S. Suri
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, California
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, California
- Department of Electrical Engineering, University of Idaho, Moscow, Idaho
| |
Collapse
|
8
|
Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Med Biol Eng Comput 2016; 55:1415-1434. [PMID: 27943087 DOI: 10.1007/s11517-016-1601-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 11/28/2016] [Indexed: 10/20/2022]
Abstract
Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients' left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system.
Collapse
|
9
|
Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches. J Med Syst 2016; 40:182. [PMID: 27299355 DOI: 10.1007/s10916-016-0543-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 06/08/2016] [Indexed: 10/21/2022]
Abstract
The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.
Collapse
|
10
|
Bastida-Jumilla M, Menchón-Lara R, Morales-Sánchez J, Verdú-Monedero R, Larrey-Ruiz J, Sancho-Gómez J. Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.08.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
11
|
A review of ultrasound common carotid artery image and video segmentation techniques. Med Biol Eng Comput 2014; 52:1073-93. [PMID: 25284219 DOI: 10.1007/s11517-014-1203-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 09/22/2014] [Indexed: 10/24/2022]
|