Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation

Shi, Changfa; Cheng, Yuanzhi; Wang, Jinke; Wang, Yadong; Mori, Kensaku; Tamura, Shinichi

doi:10.1016/j.media.2017.02.008

Cited by 67 publications

(34 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although promising results were obtained for six of the OARs, segmentation of the rectum and femoral head remain a challenging task owing to a lack of distinct boundaries and intensity information. As indicated, deformable models have both benefits and drawbacks in handling the task of OAR segmentation; however, using model-based segmentation tools along with active contours, the shortcomings presented in this paper might be overcome [35][36][37]. A possible optimal solution could be using a coupled framework to utilize the benefits of both active contours and atlas-based algorithms.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

Astaraki

Severgnini

Milan

et al. 2018

Physics and Imaging in Radiation Oncology

View full text Add to dashboard Cite

Background and purpose: In radiation therapy, defining the precise borders of cancerous tissues and adjacent normal organs has a significant effect on the therapy outcome. Deformable models offer a unique and robust approach to medical image segmentation. The objective of this study was to investigate the reliability of segmenting organs-at-risk (OARs) using three well-known local region-based level-set techniques. Methods and materials: A total of 1340 non-enhanced and enhanced planning computed tomography (CT) slices of eight OARs (the bladder, rectum, kidney, clavicle, humeral head, femoral head, spinal cord, and lung) were segmented by using local region-based active contour, local Chan-Vese, and local Gaussian distribution models. Quantitative metrics, namely Hausdorff Distance (HD), Mean Absolute Distance (MAD), Dice coefficient (DC), Percentage Volume Difference (PVD) and Absolute Volumetric Difference (AVD), were adopted to measure the correspondence between detected contours and the manual references drawn by experts. Results: The results showed the feasibility of using local region-based active contour methods for defining six of the OARs (the bladder, kidney, clavicle, humeral head, spinal cord, and lung) when adequate intensity information is available. While the most accurate results were achieved for lung (DC = 0.94) and humeral head (DC = 0.92), a poor level of agreement (DC < 0.7) was obtained for both rectum and femur. Conclusion: Incorporating local statistical information in level set methods yields to satisfactory results of OARs delineation when adequate intensity information exists between the organs. However, the complexity of adjacent organs and the lack of distinct boundaries would result in a considerable segmentation error.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

Astaraki

Severgnini

Milan

et al. 2018

Physics and Imaging in Radiation Oncology

View full text Add to dashboard Cite

show abstract

“…In general, these techniques can be divided into three classifications: (i) pattern classification technique [12,16], (ii) region growing technique [1,11], and (iii) thresholding technique [10,13,14]. The pattern classification technique requires an extensive number of test information preparations and necessities to separate the components; thus, its handling time is longer [21,22,26]. It cannot meet the continuous requirements of the CAD framework in clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy

Kumar¹,

Latte

2019

Journal of Intelligent Systems

View full text Add to dashboard Cite

The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional (2D) optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomography slices having juxtapleural nodules and 115 lung parenchyma scans are used to verify the robustness and accuracy of the proposed method. Results are compared with the most cited active contour methods. Empirical results show that the proposed fully automated method for segmenting lung parenchyma is more accurate. The proposed method is 100% sensitive to the inclusion of nodules/tumors adhering to the lung pleural wall, the juxtapleural nodule segmentation is >98%, and the lung parenchyma segmentation accuracy is >96%.

show abstract

“…Therefore, the precision of BUS segmentation directly affects the performance of the quantitative analysis and diagnosis of tumors. Segmentation is a common and crucial task in medical image analysis, and many medical image segmentation tasks share essentially similar segmentation approaches; however, comparing with medical image segmentation for other imaging modalities, e.g., computed tomography (CT), magnetic resonance imaging (MRI), mammography, BUS image segmentation is very challenging because (1) ultrasound image has very low quality due to the speckle noise, low contrast, low single noise ratio (SNR) and artifacts; (2) large variations of breast structures exists among patients, which make it difficult to apply the knowledge of anatomical structures; and (3) strong priors based on tumor shape, size and echo strength are important for organ segmentation [174,180] in other imaging modalities; but these features vary largely across patients and are difficult to be applied to BUS image segmentation.…”

mentioning

confidence: 99%

Automatic breast ultrasound image segmentation: A survey

Xian

Zhang

Cheng

et al. 2018

Pattern Recognition

219

117

View full text Add to dashboard Cite

Breast cancer is one of the leading causes of cancer death among women worldwide. In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Many BUS segmentation approaches have been studied in the last two decades, and have been proved to be effective on private datasets. Currently, the advancement of BUS image segmentation seems to meet its bottleneck. The improvement of the performance is increasingly challenging, and only few new approaches were published in the last several years. It is the time to look at the field by reviewing previous approaches comprehensively and to investigate the future directions. In this paper, we study the basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages. Keyword: breast ultrasound (BUS) images; breast cancer; segmentation; benchmark; early detection; computer-aided diagnosis (CAD) IntroductionBreast cancer occurs in the highest frequency in women among all cancers, and is also one of the leading causes of cancer death worldwide [1,2]. Scientists do not definitely know what causes breast cancer yet, and only know some risk factors that can increase the likelihood of developing breast cancer: getting older, genetics, radiation exposure, dense breast tissue, alcohol consumption, etc. The key of reducing the mortality is to find signs and symptoms of breast cancer at its early stage by clinic examination [3]. Breast ultrasound (BUS) imaging has become one of the most important and effective modality for the early detection of breast cancer because of its noninvasive, nonradioactive and cost-effective nature [4]; and it is most suitable for large-scale breast cancer screening and diagnosis in low-resource countries and regions. processing approaches. The category of the others [136,138,139,140,[142][143][144][145][146] is composed of three small sub-categories, each contains only few literatures. Due to the challenging nature of the task, just using single image processing technique cannot achieve desirable results; and most successful approaches employ hybrid techniques and model biological priors.The rest of the paper is organized as follows: in section 2, the fundamental issues in BUS segmentation are discussed, e.g., denoising, interaction, biological priors modeling, validation, and the possible problemsolving strategies; in sections 3 -6, we review automatic BUS image segmentation methods by presenting the principle of each category, discussing their advantages and disadvantages, and summarizing the most valuable strategies. In section 7, we discuss the approaches of three sub-categories briefly. Section 8 gives the conclusion and the future directions. Fundamental Issues of BUS Image SegmentationBUS segmentation approaches have been studied in the last two decades extensively, and many of them achieved good performances utilizin...

show abstract

Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation

Cited by 67 publications

References 48 publications

Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy

Automatic breast ultrasound image segmentation: A survey

Contact Info

Product

Resources

About