1
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Dwivedi V, Srinivasan B, Krishnamurthi G. Physics informed contour selection for rapid image segmentation. Sci Rep 2024; 14:6996. [PMID: 38523137 PMCID: PMC10961308 DOI: 10.1038/s41598-024-57281-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/15/2024] [Indexed: 03/26/2024] Open
Abstract
Effective training of deep image segmentation models is challenging due to the need for abundant, high-quality annotations. To facilitate image annotation, we introduce Physics Informed Contour Selection (PICS)-an interpretable, physics-informed algorithm for rapid image segmentation without relying on labeled data. PICS draws inspiration from physics-informed neural networks (PINNs) and an active contour model called snake. It is fast and computationally lightweight because it employs cubic splines instead of a deep neural network as a basis function. Its training parameters are physically interpretable because they directly represent control knots of the segmentation curve. Traditional snakes involve minimization of the edge-based loss functionals by deriving the Euler-Lagrange equation followed by its numerical solution. However, PICS directly minimizes the loss functional, bypassing the Euler Lagrange equations. It is the first snake variant to minimize a region-based loss function instead of traditional edge-based loss functions. PICS uniquely models the three-dimensional (3D) segmentation process with an unsteady partial differential equation (PDE), which allows accelerated segmentation via transfer learning. To demonstrate its effectiveness, we apply PICS for 3D segmentation of the left ventricle on a publicly available cardiac dataset. We also demonstrate PICS's capacity to encode the prior shape information as a loss term by proposing a new convexity-preserving loss term for left ventricle. Overall, PICS presents several novelties in network architecture, transfer learning, and physics-inspired losses for image segmentation, thereby showing promising outcomes and potential for further refinement.
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Affiliation(s)
- Vikas Dwivedi
- Atmospheric Science Research Center, State University of New York, Albany, NY, 12222, USA.
| | - Balaji Srinivasan
- Department of Mechanical Engineering, Indian Institute of Technology, Madras, Chennai, 600036, India
- Wadhwani School of Data Science and AI, Indian Institute of Technology, Madras, Chennai, 600036, India
| | - Ganapathy Krishnamurthi
- Department of Engineering Design, Indian Institute of Technology, Madras, Chennai, 600036, India
- Wadhwani School of Data Science and AI, Indian Institute of Technology, Madras, Chennai, 600036, India
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2
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Wang B, Yang J, Zhou Y, Yang Y, Tian X, Zhang G, Zhang X. LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation. Phys Med Biol 2024; 69:015026. [PMID: 38048633 DOI: 10.1088/1361-6560/ad1212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 12/04/2023] [Indexed: 12/06/2023]
Abstract
Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.
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Affiliation(s)
- Bing Wang
- College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, People's Republic of China
- Hebei Key Laboratory of machine Learning and Computational Intelligence, Hebei University, Baoding, 071000, Hebei, People's Republic of China
| | - Jie Yang
- College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, People's Republic of China
| | - Yunlai Zhou
- College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, People's Republic of China
| | - Ying Yang
- Hebei University Affiliated Hospital, Baoding, 071000, Hebei, People's Republic of China
| | - Xuedong Tian
- College of Cyber Security and Computer, Hebei University, Baoding, 071000, Hebei, People's Republic of China
| | - Guochun Zhang
- Hebei Key Laboratory of machine Learning and Computational Intelligence, Hebei University, Baoding, 071000, Hebei, People's Republic of China
| | - Xin Zhang
- College of Electronic Information Engineering, Hebei University, Baoding, 071000, Hebei, People's Republic of China
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Zhao S, Wang J, Wang X, Wang Y, Zheng H, Chen B, Zeng A, Wei F, Al-Kindi S, Li S. Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios. Med Image Anal 2023; 89:102906. [PMID: 37499333 DOI: 10.1016/j.media.2023.102906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 07/07/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates. Extending the target contours' capture range and providing morphology-aware parameter maps are crucial for deep ACM-based methods. For this purpose, we propose a novel Attractive Deep Morphology-aware actIve contouR nEtwork (ADMIRE) that embeds an elaborated contour attraction term (CAT) and a comprehensive contour quality (CCQ) loss into the deep ACM-based framework. The CAT adaptively extends the target contours' capture range by designing an all-to-all force field to enable the target contours' energy to contribute to farther locations. Furthermore, the CCQ loss is carefully designed to generate morphology-aware active contour parameters by simultaneously supervising the contour shape, tension, and smoothness. These designs, in cooperation with the deep ACM-based framework, enable robustness to data heterogeneity, image characteristics complexity, and target contour morphology variations. Furthermore, the deep ACM-based ADMIRE is able to cooperate well with semi-supervised strategies such as mean teacher, which enables its function in semi-supervised scenarios. ADMIRE is trained and evaluated on four challenging datasets, including three spinal datasets with more than 1000 heterogeneous images and more than 10000 vertebrae bodies, as well as a cardiac dataset with both normal and pathological cases. Results show ADMIRE achieves state-of-the-art performance on all datasets, which proves ADMIRE's accuracy, robustness, and generalization ability.
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Affiliation(s)
- Shen Zhao
- Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China
| | - Jinhong Wang
- Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China
| | - Xinxin Wang
- Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China
| | - Yikang Wang
- Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China
| | - Hanying Zheng
- Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China
| | - Bin Chen
- Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Zhejiang, China.
| | - An Zeng
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
| | - Fuxin Wei
- Department of Orthopedics, the Seventh Affiliated Hospital of Sun Yet-sen University, Shen Zhen, China
| | - Sadeer Al-Kindi
- School of Medicine, Case Western Reserve University, Cleveland, USA
| | - Shuo Li
- School of Medicine, Case Western Reserve University, Cleveland, USA
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4
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Sun X, Yang H, Wu N, Scott TC, Zhang J, Zhang W. Snake net with a neural network for detecting multiple phases in the phase diagram. Phys Rev E 2023; 107:065303. [PMID: 37464612 DOI: 10.1103/physreve.107.065303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 04/26/2023] [Indexed: 07/20/2023]
Abstract
Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett. 120, 176401 (2018)0031-900710.1103/PhysRevLett.120.176401]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this work we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, a balloon force is introduced, which helps the snake to leave a wrong initial position and thus may allow for greater freedom in the initialization of the snake. Our method is helpful in determining the phase diagram with multiple phases using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.
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Affiliation(s)
- Xiaodong Sun
- College of Physics and Optoelectronics, Taiyuan University of Technology, Shanxi 030024, China
| | - Huijiong Yang
- College of Data Science, Taiyuan University of Technology, Shanxi 030024, China
| | - Nan Wu
- College of Physics and Optoelectronics, Taiyuan University of Technology, Shanxi 030024, China
- School for Physical Sciences, University of Science and Technology of China, Hefei 230026, China
| | - T C Scott
- Institut für Physikalische Chemie, RWTH Aachen University, Aachen 52056, Germany
| | - Jie Zhang
- College of Physics and Optoelectronics, Taiyuan University of Technology, Shanxi 030024, China
| | - Wanzhou Zhang
- College of Physics and Optoelectronics, Taiyuan University of Technology, Shanxi 030024, China
- Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China
- CAS Key Laboratory for Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
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Rahman A, Ali H, Badshah N, Zakarya M, Hussain H, Rahman IU, Ahmed A, Haleem M. Power mean based image segmentation in the presence of noise. Sci Rep 2022; 12:21177. [PMID: 36477447 PMCID: PMC9729210 DOI: 10.1038/s41598-022-25250-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.
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Affiliation(s)
- Afzal Rahman
- grid.266976.a0000 0001 1882 0101Department of Mathematics, University of Peshawar,
Peshawar, Pakistan
| | - Haider Ali
- grid.266976.a0000 0001 1882 0101Department of Mathematics, University of Peshawar,
Peshawar, Pakistan
| | - Noor Badshah
- grid.444992.60000 0004 0609 495XDepartment of Basic Sciences, University of Engineering and Technology Peshawar,
Peshawar, Pakistan
| | - Muhammad Zakarya
- grid.440522.50000 0004 0478 6450Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Hameed Hussain
- Department of Computer Science, University of Buner,
Buner, Pakistan
| | - Izaz Ur Rahman
- grid.440522.50000 0004 0478 6450Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Aftab Ahmed
- grid.440522.50000 0004 0478 6450Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Muhammad Haleem
- grid.448672.b0000 0004 0569 2552Department of Computer Science, Kardan University, Kabul, Afghanistan
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6
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DH-GAC: deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07945-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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Devulapally A, Parekh V, Pazhayidam George C, Balakrishnan S. On the Variability in Cell and Nucleus Shapes. Cells Tissues Organs 2022; 213:96-107. [PMID: 36315993 DOI: 10.1159/000527825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 10/26/2022] [Indexed: 02/17/2024] Open
Abstract
Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.
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Affiliation(s)
- Anusha Devulapally
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
| | - Varun Parekh
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
| | - Clint Pazhayidam George
- School of Mathematics and Computer Science, Indian Institute of Technology Goa, Veling, India
- School of Interdisciplinary Life Sciences, Indian Institute of Technology Goa, Veling, India
| | - Sreenath Balakrishnan
- School of Interdisciplinary Life Sciences, Indian Institute of Technology Goa, Veling, India
- School of Mechanical Sciences, Indian Institute of Technology Goa, Veling, India
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8
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Wan J, Yong B, Zhou X. Water extraction from SAR images based on improved geodesic active contour. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:698. [PMID: 35986795 DOI: 10.1007/s10661-022-10366-4] [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: 02/12/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The rapid and accurate acquisition of water body information is of great significance to water resource investigation, flood disaster monitoring, ecological environment protection, and other fields. In this paper, the water boundary is optimized and extracted from single-polarization SAR images based on an improved geodesic active contour model (IMGAC). Firstly, the rough extraction results of the water body were obtained according to the adaptive threshold, and then a narrowband model was established, and the signed pressure force (SPF) function was introduced into the geodesic active contour (GAC) model. Finally, the optimal water boundary was obtained through continuous iteration. Compared with the active contour (AC) model without edge and the traditional GAC model, the results show that the IMGAC model proposed in this paper can reduce the calculation efficiency and improve the accuracy of water boundary detection. The F-measure index was used to evaluate the extraction accuracy of the three methods. IMGAC method had the highest extraction accuracy, which was 96.43%. The kappa coefficient reached 0.929. The F-measure index was 96.20%. Our study can provide a reference for water extraction and water boundary optimization.
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Affiliation(s)
- Jikang Wan
- School of Computer and Information, Hohai University, Nanjing, 211100, China.
| | - Bin Yong
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 211100, China
| | - Xiaofeng Zhou
- School of Computer and Information, Hohai University, Nanjing, 211100, China
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10
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Bougrine A, Harba R, Canals R, Ledee R, Jabloun M, Villeneuve A. Segmentation of Plantar Foot Thermal Images Using Prior Information. SENSORS (BASEL, SWITZERLAND) 2022; 22:3835. [PMID: 35632244 PMCID: PMC9146771 DOI: 10.3390/s22103835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients.
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Affiliation(s)
- Asma Bougrine
- Multidisciplinary Research Laboratory in Systems Engineering, Mechanics and Energy (PRISME), University of Orleans, 12 rue de Blois, 45067 Orleans, France; (R.H.); (R.C.); (R.L.); (M.J.)
| | - Rachid Harba
- Multidisciplinary Research Laboratory in Systems Engineering, Mechanics and Energy (PRISME), University of Orleans, 12 rue de Blois, 45067 Orleans, France; (R.H.); (R.C.); (R.L.); (M.J.)
| | - Raphael Canals
- Multidisciplinary Research Laboratory in Systems Engineering, Mechanics and Energy (PRISME), University of Orleans, 12 rue de Blois, 45067 Orleans, France; (R.H.); (R.C.); (R.L.); (M.J.)
| | - Roger Ledee
- Multidisciplinary Research Laboratory in Systems Engineering, Mechanics and Energy (PRISME), University of Orleans, 12 rue de Blois, 45067 Orleans, France; (R.H.); (R.C.); (R.L.); (M.J.)
| | - Meryem Jabloun
- Multidisciplinary Research Laboratory in Systems Engineering, Mechanics and Energy (PRISME), University of Orleans, 12 rue de Blois, 45067 Orleans, France; (R.H.); (R.C.); (R.L.); (M.J.)
| | - Alain Villeneuve
- The Diabetic Foot Service, Regional Hospital of Orleans, 45100 Orleans, France;
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11
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Hidden Markov modeling for maximum probability neuron reconstruction. Commun Biol 2022; 5:388. [PMID: 35468989 PMCID: PMC9038756 DOI: 10.1038/s42003-022-03320-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/24/2022] [Indexed: 11/08/2022] Open
Abstract
Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron fluorescence. ViterBrain utilizes dynamic programming to compute the global maximizer of what we call the most probable neuron path. We applied our algorithm to imperfect image segmentations, and showed that it can follow axons in the presence of noise or nearby neurons. We also provide an interactive framework where users can trace neurons by fixing start and endpoints. ViterBrain is available in our open-source Python package brainlit. ViterBrain is an automated probabilistic reconstruction method that can reconstruct neuronal geometry and processes from microscopy images with code available in the open-source Python package, brainlit.
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12
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Water-Body Segmentation for SAR Images: Past, Current, and Future. REMOTE SENSING 2022. [DOI: 10.3390/rs14071752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.
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13
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Image Segmentation with a Priori Conditions: Applications to Medical and Geophysical Imaging. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2022. [DOI: 10.3390/mca27020026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In this paper, we propose a method for semi-supervised image segmentation based on geometric active contours. The main novelty of the proposed method is the initialization of the segmentation process, which is performed with a polynomial approximation of a user defined initialization (for instance, a set of points or a curve to be interpolated). This work is related to many potential applications: the geometric conditions can be useful to improve the quality the segmentation process in medicine and geophysics when it is required (weak contrast of the image, missing parts in the image, non-continuous contour…). We compare our method to other segmentation algorithms, and we give experimental results related to several medical and geophysical applications.
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14
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Harrison K, Pullen H, Welsh C, Oktay O, Alvarez-Valle J, Jena R. Machine Learning for Auto-Segmentation in Radiotherapy Planning. Clin Oncol (R Coll Radiol) 2022; 34:74-88. [PMID: 34996682 DOI: 10.1016/j.clon.2021.12.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/27/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022]
Abstract
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability. Automatic segmentation (auto-segmentation), using computer algorithms, seeks to address these issues. Advances in machine learning and computer vision have led to the development of methods for accurate and efficient auto-segmentation. This review surveys auto-segmentation techniques and applications in radiotherapy planning. It provides an overview of traditional approaches to auto-segmentation, including intensity analysis, shape modelling and atlas-based methods. The focus, though, is on uses of machine learning and deep learning, including convolutional neural networks. Finally, the future of machine-learning-driven auto-segmentation in clinical settings is considered, and the barriers that must be overcome for it to be widely accepted into routine practice are highlighted.
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Affiliation(s)
- K Harrison
- Cavendish Laboratory, University of Cambridge, Cambridge, UK.
| | - H Pullen
- Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - C Welsh
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - O Oktay
- Health Intelligence, Microsoft Research, Cambridge, UK
| | | | - R Jena
- Department of Oncology, University of Cambridge, Cambridge, UK; Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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15
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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 2022. [DOI: 10.1007/s10462-021-10121-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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16
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Wang S, Liu X, Zhao J, Liu Y, Liu S, Liu Y, Zhao J. Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106265. [PMID: 34311415 DOI: 10.1016/j.cmpb.2021.106265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Cholangiocarcinoma (CCA) is one of the most aggressive human malignant tumors and is becoming one of the main factors of death and disability globally. Specifically, 60% to 70% of CCA patients were diagnosed with local invasion or distant metastasis and lost the chance of radical operation. The overall median survival time was less than 12 months. As a non-invasive diagnostic technology, medical imaging consisting of computed tomography (CT) imaging, magnetic resonance imaging (MRI), and ultrasound (US) imaging, is the most effectively and commonly used method to detect CCA. The computer auxiliary diagnosis (CAD) system based on medical imaging is helpful for rapid diagnosis and provides credible "second opinion" for specialists. The purpose of this review is to categorize and review the CAD technique of detecting CCA based on medical imaging. METHODS This work applies a four-level screening process to choose suitable publications. 125 research papers published in different academic research databases were selected and analyzed according to specific criteria. From the five steps of medical image acquisition, processing, analysis, understanding and verification of CAD combined with artificial intelligence algorithms, we obtain the most advanced insights related to CCA detection. RESULTS This work provides a comprehensive analysis and comparison analysis of the current CAD systems of detecting CCA. After careful investigation, we find that the main detection methods are traditional machine learning method and deep learning method. For the detection, the most commonly used method is semi-automatic segmentation algorithm combined with support vector machine classifier method, combination of which has good detection performance. The end-to-end training mode makes deep learning method more and more popular in CAD systems. However, due to the limited medical training data, the accuracy of deep learning method is unsatisfactory. CONCLUSIONS Based on analysis of artificial intelligence methods applied in CCA, this work is expected to be truly applied in clinical practice in the future to improve the level of clinical diagnosis and treatment of it. This work concludes by providing a prediction of future trends, which will be of great significance for researchers in the medical imaging of CCA and artificial intelligence.
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Affiliation(s)
- Shiyu Wang
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xiang Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Jingwen Zhao
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Yiwen Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Shuhong Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Yisi Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Jingmin Zhao
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China.
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Chen D, Zhu J, Zhang X, Shu M, Cohen LD. Geodesic Paths for Image Segmentation With Implicit Region-Based Homogeneity Enhancement. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:5138-5153. [PMID: 34014824 DOI: 10.1109/tip.2021.3078106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
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Chen D, Spencer J, Mirebeau JM, Chen K, Shu M, Cohen LD. A Generalized Asymmetric Dual-Front Model for Active Contours and Image Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:5056-5071. [PMID: 33979285 DOI: 10.1109/tip.2021.3078102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The Voronoi diagram-based dual-front scheme is known as a powerful and efficient technique for addressing the image segmentation and domain partitioning problems. In the basic formulation of existing dual-front approaches, the evolving contour can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a new dual-front model based on asymmetric quadratic metrics. These metrics considered are built by the integration of the image features and a vector field derived from the evolving contour. The use of the asymmetry enhancement can reduce the risk for the segmentation contours being stuck at false positions, especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.
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Yahia Lahssene Y, Meddeber L, Zouagui T, Jennane R. A topology constrained geometric deformable model for medical image segmentation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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20
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From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09924-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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Magnetostatic Active Contour Model with Classification Method of Sparse Representation. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2020. [DOI: 10.1155/2020/5438763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The active contour model is widely used to segment images. For the classical magnetostatic active contour (MAC) model, the magnetic field is computed based on the detected points by using an edge detector. However, noise and nontarget points are always detected. Thus, MAC is nonrobust to noise and the extracted objects may be deviant from the real objects. In this paper, a magnetostatic active contour model with a classification method of sparse representation is proposed. First, rough edge information is obtained with some edge detectors. Second, the extracted edge contours are divided into two parts by sparse classification, that is, the target object part and the redundant part. Based on the classified target points, a new magnetic field is generated, and contours evolve with MAC to extract the target objects. Experimental results show that the proposed model could decrease the influence of noise and robust segmentation results could be obtained.
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Jodas DS, da Costa MFM, Parreira TAA, Pereira AS, Tavares JMRS. Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images. Comput Biol Med 2020; 123:103901. [PMID: 32658794 DOI: 10.1016/j.compbiomed.2020.103901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 10/23/2022]
Abstract
Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.
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Affiliation(s)
- Danilo Samuel Jodas
- CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| | - Maria Francisca Monteiro da Costa
- IFE Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Tiago A A Parreira
- AH Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Aledir Silveira Pereira
- Universidade Estadual Paulista Júlio de Mesquita Filho, Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
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Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image. REMOTE SENSING 2020. [DOI: 10.3390/rs12111702] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.
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Dutta A, Engels J, Hahn M. Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019; 41:3034-3047. [PMID: 30222551 DOI: 10.1109/tpami.2018.2869744] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Normalized Cut is a well-established divisive image segmentation method, which we adapt in this paper for the segmentation of laser point clouds in urban areas. Our focus is on polyhedral objects with planar surfaces. Due to its target function, Normalized Cut favours cuts with "short cut lines" or "small cut surfaces", which is a drawback for our application. We therefore modify the target function, weighting the similarity measures with distance-dependent weights. We call the induced minimization problem "Distance-weighted Cut" (DWCut). The new target function leads to a generalized eigenvalue problem, which is slightly more complicated than the corresponding problem for the Normalized Cut; on the other hand, the new target function is easier to interpret and avoids some drawbacks of the Normalized Cut. We point out an efficient method for the numerical solution of the eigenvalue problem which is based on a Krylov subspace method. DWCut can be beneficially combined with an aggregation in order to reduce the computational effort and to avoid shortcomings due to insufficient plane parameters. We present examples for the successful application of the Distance-weighted Cut principle and evaluate its results by comparison with the results of corresponding manual segmentations.
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25
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A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019; 61:247-259. [DOI: 10.1016/j.mri.2019.05.043] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 05/30/2019] [Accepted: 05/30/2019] [Indexed: 01/17/2023]
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Araujo RJ, Fernandes K, Cardoso JS. Sparse Multi-Bending Snakes. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:3898-3909. [PMID: 30843808 DOI: 10.1109/tip.2019.2902832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperability turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.
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Chien HJ, Chang CJ. Application of the Balloon Snake in the Volume Measurement of Subretinal Fluid in Central Serous Chorioretinopathy. Semin Ophthalmol 2019; 34:403-408. [PMID: 31288617 DOI: 10.1080/08820538.2019.1640749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Purpose: To apply the Balloon Snake to detect the edge and measure the volume of subretinal fluid (SRF) from spectral domain optical coherence tomography (SD-OCT) images of patients with central serous chorioretinopathy (CSC). Methods: A total of 20 CSC patients whose SD-OCT images collected from their unilateral eyes were enrolled for the study. An image analysis program developed based on the Balloon Snake was used to detect the edge and to measure the volume of SRF. Results: Good agreement was found between the manual segmentation and the Balloon Snake-based method (intraclass correlation coefficient = 0.994). For each volume measurement, the mean time used by the Balloon Snake-based method was 130.5 ± 63.2 (range 54.0 to 227.3) seconds, approximately 30 min faster than the manual segmentation. Conclusion: The Balloon Snake-based method produced accurate and time-efficient volume measurement of SRF in patients with CSC.
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Affiliation(s)
- Hung-Jen Chien
- a Department of Ophthalmology, Taichung Veterans General Hospital , Taichung City , Taiwan
| | - Chia-Jen Chang
- a Department of Ophthalmology, Taichung Veterans General Hospital , Taichung City , Taiwan.,b Department of Optometry, Central Taiwan University of Science and Technology , Taichung City , Taiwan
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Wang B, Yuan X, Gao X, Li X, Tao D. A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:1558-1569. [PMID: 29994789 DOI: 10.1109/tcyb.2018.2799999] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.
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Song Y, Peng G. A fast two-stage active contour model for intensity inhomogeneous image segmentation. PLoS One 2019; 14:e0214851. [PMID: 31002667 PMCID: PMC6474649 DOI: 10.1371/journal.pone.0214851] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/21/2019] [Indexed: 11/21/2022] Open
Abstract
This paper presents a fast two-stage image segmentation method for intensity inhomogeneous image using an energy function based on a local region-based active contour model with exponential family. In the first stage, we preliminary segment the down-sampled images by the local correntropy-based K-means clustering model with exponential family, which can fast obtain a coarse result with low computational complexity. Subsequently, by taking the up-sampled contour of the first stage as initialization, we precisely segment the original images by the improved local correntropy-based K-means clustering model with exponential family in the second stage. This stage can achieve accurate result rapidly as the result of the proper initialization. Meanwhile, we converge the energy function of two-stage by the Riemannian steepest descent method. Comparing with other statistical numerically methods, which are used to solve the partial differential equations(PDEs), this method can obtain the global minima with less iterations. Moreover, to promote regularity of energy function, we use a popular regular method which is an inner product and applies spatial smoothing to the gradient flow. Extensive experiments on synthetic and real images demonstrate that the proposed method is more efficient than the other state-of-art methods on intensity inhomogeneous images.
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Affiliation(s)
- Yangyang Song
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, PR China
- * E-mail:
| | - Guohua Peng
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, PR China
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Wang YY, Peng WX, Qiu CH, Jiang J, Xia SR. Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images. ULTRASONICS 2019; 92:1-7. [PMID: 30205179 DOI: 10.1016/j.ultras.2018.06.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 05/25/2018] [Accepted: 06/16/2018] [Indexed: 06/08/2023]
Abstract
Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.
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Affiliation(s)
- Yuan-Yuan Wang
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Wen-Xian Peng
- Radiology Department of Hangzhou Medical College, China
| | - Chen-Hui Qiu
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Jun Jiang
- Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Shun-Ren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China.
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Ahmady Phoulady H, Goldgof D, Hall LO, Nash KR, Mouton PR. Automatic stereology of mean nuclear size of neurons using an active contour framework. J Chem Neuroanat 2019; 96:110-115. [PMID: 30630013 DOI: 10.1016/j.jchemneu.2018.12.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 12/31/2018] [Accepted: 12/31/2018] [Indexed: 01/20/2023]
Abstract
The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 μm. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R2 ≥ 0.95) with a 5× gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.
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Affiliation(s)
- Hady Ahmady Phoulady
- Department of Computer Science, University of Southern Maine, Portland, ME, United States.
| | - Dmitry Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, United States
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, United States
| | - Kevin R Nash
- Department of Molecular Pharmacology and Physiology, University of South Florida, Tampa, FL, United States
| | - Peter R Mouton
- Byrd Alzheimer's Disease Center and Research Institute, University of South Florida, Tampa, FL, United States; SRC Biosciences, Tampa, FL, United States
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Machado S, Mercier V, Chiaruttini N. LimeSeg: a coarse-grained lipid membrane simulation for 3D image segmentation. BMC Bioinformatics 2019; 20:2. [PMID: 30606118 PMCID: PMC6318983 DOI: 10.1186/s12859-018-2471-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 11/06/2018] [Indexed: 11/15/2022] Open
Abstract
Background 3D segmentation is often a prerequisite for 3D object display and quantitative measurements. Yet existing voxel-based methods do not directly give information on the object surface or topology. As for spatially continuous approaches such as level-set, active contours and meshes, although providing surfaces and concise shape description, they are generally not suitable for multiple object segmentation and/or for objects with an irregular shape, which can hamper their adoption by bioimage analysts. Results We developed LimeSeg, a computationally efficient and spatially continuous 3D segmentation method. LimeSeg is easy-to-use and can process many and/or highly convoluted objects. Based on the concept of SURFace ELements (“Surfels”), LimeSeg resembles a highly coarse-grained simulation of a lipid membrane in which a set of particles, analogous to lipid molecules, are attracted to local image maxima. The particles are self-generating and self-destructing thus providing the ability for the membrane to evolve towards the contour of the objects of interest. The capabilities of LimeSeg: simultaneous segmentation of numerous non overlapping objects, segmentation of highly convoluted objects and robustness for big datasets are demonstrated on experimental use cases (epithelial cells, brain MRI and FIB-SEM dataset of cellular membrane system respectively). Conclusion In conclusion, we implemented a new and efficient 3D surface reconstruction plugin adapted for various sources of images, which is deployed in the user-friendly and well-known ImageJ environment.
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Affiliation(s)
- Sarah Machado
- Marcos González Gaitán lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland
| | - Vincent Mercier
- Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland
| | - Nicolas Chiaruttini
- Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland.
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Virtual M-Mode for Echocardiography: A New Approach for the Segmentation of the Anterior Mitral Leaflet. IEEE J Biomed Health Inform 2019; 23:305-313. [DOI: 10.1109/jbhi.2018.2799738] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Borga M. MRI adipose tissue and muscle composition analysis-a review of automation techniques. Br J Radiol 2018; 91:20180252. [PMID: 30004791 PMCID: PMC6223175 DOI: 10.1259/bjr.20180252] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/12/2018] [Accepted: 07/09/2018] [Indexed: 02/06/2023] Open
Abstract
MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.
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Affiliation(s)
- Magnus Borga
- Department
of Biomedical Engineering and Center for Medical Image Science and
Visualization (CMIV), Linköping University,
Linköping, Sweden
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Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice. Sci Rep 2018; 8:12108. [PMID: 30108260 PMCID: PMC6092416 DOI: 10.1038/s41598-018-30557-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 08/01/2018] [Indexed: 11/08/2022] Open
Abstract
Homologous recombination (HR) events are key drivers of cancer-promoting mutations, and the ability to visualize these events in situ provides important information regarding mutant cell type, location, and clonal expansion. We have previously created the Rosa26 Direct Repeat (RaDR) mouse model wherein HR at an integrated substrate gives rise to a fluorescent cell. To fully leverage this in situ approach, we need better ways to quantify rare fluorescent cells deep within tissues. Here, we present a robust, automated event quantification algorithm that uses image intensity and gradient features to detect fluorescent cells in deep tissue specimens. To analyze the performance of our algorithm, we simulate fluorescence behavior in tissue using Monte Carlo methods. Importantly, this approach reduces the potential for bias in manual counting and enables quantification of samples with highly dense HR events. Using this approach, we measured the relative frequency of HR within a chromosome and between chromosomes and found that HR within a chromosome is more frequent, which is consistent with the close proximity of sister chromatids. Our approach is both objective and highly rapid, providing a powerful tool, not only to researchers interested in HR, but also to many other researchers who are similarly using fluorescence as a marker for understanding mammalian biology in tissues.
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Yang Y, Tian D, Wu B. A fast and reliable noise-resistant medical image segmentation and bias field correction model. Magn Reson Imaging 2018; 54:15-31. [PMID: 30075185 DOI: 10.1016/j.mri.2018.06.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/21/2018] [Accepted: 06/21/2018] [Indexed: 11/30/2022]
Abstract
In recent years, with the rapid development of modern medical image technology, the medical image processing technology is becoming more important. In particular, the accurate segmentation of medical images is significant for doctors to diagnose and analyze the etiology. However, the false contours appearing in medical images due to fuzzy image boundary, intensity inhomogeneity and random noise, may lead to the inaccurate segmentation results. In this paper, an improved active contour model based on global image information is proposed, which can accurately segment images disturbed by intensity inhomogeneities and serious noise. We give the two-phase energy functional and multi-phase energy functional of our model, and apply it to segment magnetic resonance (MR) images, ultrasound (US) images and synthetic images. Experimental results and comparisons with other models have shown that our model has the advantages of higher accuracy, higher efficiency and robustness in dealing with the intensity inhomogeneity and serious noise in image segmentation.
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Affiliation(s)
- Yunyun Yang
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
| | - Dongcai Tian
- School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Boying Wu
- Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
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An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0629] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Abstract
Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.
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Ma L, Kiyomatsu H, Nakagawa K, Wang J, Kobayashi E, Sakuma I. Accurate vessel segmentation in ultrasound images using a local-phase-based snake. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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39
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Badura P. Virtual bacterium colony in 3D image segmentation. Comput Med Imaging Graph 2018; 65:152-166. [DOI: 10.1016/j.compmedimag.2017.04.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 04/13/2017] [Accepted: 04/21/2017] [Indexed: 11/16/2022]
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Luo S, Tong L, Chen Y. A Multi-Region Segmentation Method for SAR Images based on the Multi-Texture Model with Level Sets. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:2560-2574. [PMID: 29994632 DOI: 10.1109/tip.2018.2806201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Synthetic Aperture Radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the Ratio of Exponentially Weighted Averages (ROEWA) operator is modified to obtain edge feature. Region information is obtained by the Improved Edgeworth Series Expansion (IESE), which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.
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41
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Bharath R, Rajalakshmi P, Mateen MA. Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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42
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Qian C, Yang X. An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:19-32. [PMID: 29157451 DOI: 10.1016/j.cmpb.2017.10.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 09/16/2017] [Accepted: 10/02/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Carotid artery atherosclerosis is an important cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting atherosclerotic carotid plaque in ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. In this paper, we propose and evaluate a novel learning-based integrated framework for plaque segmentation. METHODS In our study, four different classification algorithms, along with the auto-context iterative algorithm, were employed to effectively integrate features from ultrasound images and later also the iteratively estimated and refined probability maps together for pixel-wise classification. The four classification algorithms were support vector machine with linear kernel, support vector machine with radial basis function kernel, AdaBoost and random forest. The plaque segmentation was implemented in the generated probability map. The performance of the four different learning-based plaque segmentation methods was tested on 29 B-mode ultrasound images. The evaluation indices for our proposed methods were consisted of sensitivity, specificity, Dice similarity coefficient, overlap index, error of area, absolute error of area, point-to-point distance, and Hausdorff point-to-point distance, along with the area under the ROC curve. RESULTS The segmentation method integrated the random forest and an auto-context model obtained the best results (sensitivity 80.4 ± 8.4%, specificity 96.5 ± 2.0%, Dice similarity coefficient 81.0 ± 4.1%, overlap index 68.3 ± 5.8%, error of area -1.02 ± 18.3%, absolute error of area 14.7 ± 10.9%, point-to-point distance 0.34 ± 0.10 mm, Hausdorff point-to-point distance 1.75 ± 1.02 mm, and area under the ROC curve 0.897), which were almost the best, compared with that from the existed methods. CONCLUSIONS Our proposed learning-based integrated framework investigated in this study could be useful for atherosclerotic carotid plaque segmentation, which will be helpful for the measurement of carotid plaque burden.
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Affiliation(s)
- Chunjun Qian
- School of Science, Nanjing University of Science and Technology, Jiangsu, China.
| | - Xiaoping Yang
- School of Science, Nanjing University of Science and Technology, Jiangsu, China; Department of Mathematics, Nanjing University, Jiangsu, China
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The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data. INFRASTRUCTURES 2017. [DOI: 10.3390/infrastructures2030009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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44
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Sapkota M, Liu F, Xie Y, Su H, Xing F, Yang L. AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle. IEEE J Biomed Health Inform 2017; 22:942-954. [PMID: 28422672 DOI: 10.1109/jbhi.2017.2694344] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.
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Grah JS, Harrington JA, Koh SB, Pike JA, Schreiner A, Burger M, Schönlieb CB, Reichelt S. Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy. Methods 2017; 115:91-99. [PMID: 28189773 PMCID: PMC6414815 DOI: 10.1016/j.ymeth.2017.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 02/04/2017] [Accepted: 02/06/2017] [Indexed: 11/25/2022] Open
Abstract
In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.
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Affiliation(s)
- Joana Sarah Grah
- University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, United Kingdom.
| | - Jennifer Alison Harrington
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Siang Boon Koh
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Jeremy Andrew Pike
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Alexander Schreiner
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Martin Burger
- Westfälische Wilhelms-Universität Münster, Institute for Computational and Applied Mathematics, Einsteinstrasse 62, 48149 Münster, Germany
| | - Carola-Bibiane Schönlieb
- University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Stefanie Reichelt
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
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46
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Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example. REMOTE SENSING 2017. [DOI: 10.3390/rs9020140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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47
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Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:5256346. [PMID: 28191031 PMCID: PMC5274694 DOI: 10.1155/2017/5256346] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 12/10/2016] [Accepted: 12/22/2016] [Indexed: 02/05/2023]
Abstract
The hippocampus has been known as one of the most important structures referred to as Alzheimer's disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.
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Lian J, Ma Y, Ma Y, Shi B, Liu J, Yang Z, Guo Y. Automatic gallbladder and gallstone regions segmentation in ultrasound image. Int J Comput Assist Radiol Surg 2017; 12:553-568. [PMID: 28063077 DOI: 10.1007/s11548-016-1515-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 12/15/2016] [Indexed: 11/28/2022]
Abstract
PURPOSE As gallbladder diseases including gallstone and cholecystitis are mainly diagnosed by using ultra-sonographic examinations, we propose a novel method to segment the gallbladder and gallstones in ultrasound images. METHODS The method is divided into five steps. Firstly, a modified Otsu algorithm is combined with the anisotropic diffusion to reduce speckle noise and enhance image contrast. The Otsu algorithm separates distinctly the weak edge regions from the central region of the gallbladder. Secondly, a global morphology filtering algorithm is adopted for acquiring the fine gallbladder region. Thirdly, a parameter-adaptive pulse-coupled neural network (PA-PCNN) is employed to obtain the high-intensity regions including gallstones. Fourthly, a modified region-growing algorithm is used to eliminate physicians' labeled regions and avoid over-segmentation of gallstones. It also has good self-adaptability within the growth cycle in light of the specified growing and terminating conditions. Fifthly, the smoothing contours of the detected gallbladder and gallstones are obtained by the locally weighted regression smoothing (LOESS). RESULTS We test the proposed method on the clinical data from Gansu Provincial Hospital of China and obtain encouraging results. For the gallbladder and gallstones, average similarity percent of contours (EVA) containing metrics dice's similarity , overlap fraction and overlap value is 86.01 and 79.81%, respectively; position error is 1.7675 and 0.5414 mm, respectively; runtime is 4.2211 and 0.6603 s, respectively. Our method then achieves competitive performance compared with the state-of-the-art methods. CONCLUSIONS The proposed method is potential to assist physicians for diagnosing the gallbladder disease rapidly and effectively.
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Affiliation(s)
- Jing Lian
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Yide Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Yurun Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Bin Shi
- Equipment Management Department, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China
| | - Jizhao Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Zhen Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Yanan Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
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Piorkowski A, Nurzynska K, Gronkowska-Serafin J, Selig B, Boldak C, Reska D. Influence of applied corneal endothelium image segmentation techniques on the clinical parameters. Comput Med Imaging Graph 2017; 55:13-27. [DOI: 10.1016/j.compmedimag.2016.07.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 05/30/2016] [Accepted: 07/29/2016] [Indexed: 10/21/2022]
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50
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