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Tan Z, Feng J, Lu W, Yin Y, Yang G, Zhou J. Multi-task global optimization-based method for vascular landmark detection. Comput Med Imaging Graph 2024; 114:102364. [PMID: 38432060 DOI: 10.1016/j.compmedimag.2024.102364] [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: 07/16/2023] [Revised: 12/04/2023] [Accepted: 02/22/2024] [Indexed: 03/05/2024]
Abstract
Vascular landmark detection plays an important role in medical analysis and clinical treatment. However, due to the complex topology and similar local appearance around landmarks, the popular heatmap regression based methods always suffer from the landmark confusion problem. Vascular landmarks are connected by vascular segments and have special spatial correlations, which can be utilized for performance improvement. In this paper, we propose a multi-task global optimization-based framework for accurate and automatic vascular landmark detection. A multi-task deep learning network is exploited to accomplish landmark heatmap regression, vascular semantic segmentation, and orientation field regression simultaneously. The two auxiliary objectives are highly correlated with the heatmap regression task and help the network incorporate the structural prior knowledge. During inference, instead of performing a max-voting strategy, we propose a global optimization-based post-processing method for final landmark decision. The spatial relationships between neighboring landmarks are utilized explicitly to tackle the landmark confusion problem. We evaluated our method on a cerebral MRA dataset with 564 volumes, a cerebral CTA dataset with 510 volumes, and an aorta CTA dataset with 50 volumes. The experiments demonstrate that the proposed method is effective for vascular landmark localization and achieves state-of-the-art performance.
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Affiliation(s)
- Zimeng Tan
- Department of Automation, Tsinghua University, Beijing, China
| | - Jianjiang Feng
- Department of Automation, Tsinghua University, Beijing, China.
| | - Wangsheng Lu
- UnionStrong (Beijing) Technology Co.Ltd, Beijing, China
| | - Yin Yin
- UnionStrong (Beijing) Technology Co.Ltd, Beijing, China
| | | | - Jie Zhou
- Department of Automation, Tsinghua University, Beijing, China
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Zou L, Cai Z, Mao L, Nie Z, Qiu Y, Yang X. Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism. Artif Intell Med 2024; 150:102825. [PMID: 38553165 DOI: 10.1016/j.artmed.2024.102825] [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: 05/05/2023] [Revised: 01/04/2024] [Accepted: 02/23/2024] [Indexed: 04/02/2024]
Abstract
Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling algorithms usually cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. To address these two problems, we propose an Automated Peripancreatic vEssel Segmentation and lAbeling (APESA) framework, to not only highly improve the segmentation performance for PPV, but also efficiently identify the peripancreatic artery (PPA) branches. There are two core modules in our proposed APESA framework: iterative trunk growth module (ITGM) for vein segmentation and weakly supervised labeling mechanism (WSLM) for artery labeling. The ITGM is composed of a series of iterative submodules, each of which chooses the largest connected component of the previous PPV segmentation as the trunk of a tree structure, seeks for the potential missing branches around the trunk by our designed branch proposal network, and facilitates trunk growth under the connectivity constraint. The WSLM incorporates the rule-based pseudo label generation with less expert participation, an anatomical labeling network to learn the branch distribution voxel by voxel, and adaptive radius-based postprocessing to refine the branch structures of the labeling predictions. Our achieved Dice of 94.01% for PPV segmentation on our collected dataset represents an approximately 10% accuracy improvement compared to state-of-the-art methods. Additionally, we attained a Dice of 97.01% for PPA segmentation and competitive labeling performance for PPA labeling compared to prior works. Our source codes will be publicly available at https://github.com/ZouLiwen-1999/APESA.
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Affiliation(s)
- Liwen Zou
- Department of Mathematics, Nanjing University, Nanjing, 210093, China
| | - Zhenghua Cai
- Medical School, Nanjing University, Nanjing, 210007, China
| | - Liang Mao
- Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, 210008, China
| | - Ziwei Nie
- Department of Mathematics, Nanjing University, Nanjing, 210093, China
| | - Yudong Qiu
- Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, 210008, China.
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, 210093, China.
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Zhang Y, Luo G, Wang W, Cao S, Dong S, Yu D, Wang X, Wang K. TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:129-139. [PMID: 38074924 PMCID: PMC10706468 DOI: 10.1109/jtehm.2023.3329031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/05/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023]
Abstract
OBJECTIVE Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. METHODS AND PROCEDURES Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. RESULTS On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. CONCLUSION TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. CLINICAL IMPACT This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.
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Affiliation(s)
- Yuyang Zhang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
| | - Gongning Luo
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
| | - Wei Wang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
- School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518000China
| | - Shaodong Cao
- Department of RadiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Suyu Dong
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbin150040China
| | - Daren Yu
- Department of CardiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Xiaoyun Wang
- Department of CardiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Kuanquan Wang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
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Hilbert A, Rieger J, Madai VI, Akay EM, Aydin OU, Behland J, Khalil AA, Galinovic I, Sobesky J, Fiebach J, Livne M, Frey D. Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease. Front Neurol 2022; 13:1000914. [PMID: 36341105 PMCID: PMC9634733 DOI: 10.3389/fneur.2022.1000914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/22/2022] [Indexed: 11/21/2022] Open
Abstract
Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting.
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Affiliation(s)
- Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Adam Hilbert
| | - Jana Rieger
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Quality | Ethics | Open Science | Translation Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | - Ela M. Akay
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Orhun U. Aydin
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jonas Behland
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ahmed A. Khalil
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany
- Biomedical Innovation Academy, Berlin Institute of Health, Berlin, Germany
| | - Ivana Galinovic
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Sobesky
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Johanna-Etienne-Hospital, Neuss, Germany
| | - Jochen Fiebach
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michelle Livne
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes. Clin Imaging 2022; 87:43-53. [DOI: 10.1016/j.clinimag.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 03/09/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022]
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Liu Y, Wang X, Wu Z, López-Linares K, Macía I, Ru X, Zhao H, González Ballester MA, Zhang C. Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching. Med Image Anal 2021; 75:102249. [PMID: 34743037 DOI: 10.1016/j.media.2021.102249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/14/2021] [Accepted: 09/17/2021] [Indexed: 10/20/2022]
Abstract
Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.
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Affiliation(s)
- Yue Liu
- School of Artificial Intelligence, Beijing Normal University, China
| | - Xingce Wang
- School of Artificial Intelligence, Beijing Normal University, China.
| | - Zhongke Wu
- School of Artificial Intelligence, Beijing Normal University, China.
| | - Karen López-Linares
- Vicomtech Foundation, San Sebastián, Spain; Biodonostia Health Research Institute, San Sebastián, Spain; BCN MedTech, Dept. of Information and Communication Technologies, Universitát Pompeu Fabra, Barcelona, Spain
| | - Iván Macía
- Vicomtech Foundation, San Sebastián, Spain; Biodonostia Health Research Institute, San Sebastián, Spain
| | - Xudong Ru
- School of Artificial Intelligence, Beijing Normal University, China
| | - Haichuan Zhao
- School of Artificial Intelligence, Beijing Normal University, China
| | - Miguel A González Ballester
- BCN MedTech, Dept. of Information and Communication Technologies, Universitát Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
| | - Chong Zhang
- BCN MedTech, Dept. of Information and Communication Technologies, Universitát Pompeu Fabra, Barcelona, Spain.
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Hashizume M. Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence. CYBORG AND BIONIC SYSTEMS 2021; 2021:9160478. [PMID: 36285135 PMCID: PMC9494695 DOI: 10.34133/2021/9160478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 12/03/2020] [Indexed: 02/19/2024] Open
Abstract
Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of "dynamic living human anatomy." It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.
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Daldrup-Link H. Artificial intelligence applications for pediatric oncology imaging. Pediatr Radiol 2019; 49:1384-1390. [PMID: 31620840 PMCID: PMC6820135 DOI: 10.1007/s00247-019-04360-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 12/21/2018] [Accepted: 02/14/2019] [Indexed: 12/27/2022]
Abstract
Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community.
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Affiliation(s)
- Heike Daldrup-Link
- Department of Radiology, Lucile Packard Children's Hospital, Pediatric Molecular Imaging Program, Stanford University School of Medicine, 725 Welch Road, Room 1665, Stanford, CA, 94305-5614, USA. .,Department of Pediatrics, Hematology/Oncology Section, Stanford University School of Medicine, Stanford, CA, USA.
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Wu D, Wang X, Bai J, Xu X, Ouyang B, Li Y, Zhang H, Song Q, Cao K, Yin Y. Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int J Comput Assist Radiol Surg 2018; 14:271-280. [DOI: 10.1007/s11548-018-1884-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 11/05/2018] [Indexed: 10/27/2022]
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Kitasaka T, Kagajo M, Nimura Y, Hayashi Y, Oda M, Misawa K, Mori K. Automatic anatomical labeling of arteries and veins using conditional random fields. Int J Comput Assist Radiol Surg 2017; 12:1041-1048. [PMID: 28275889 DOI: 10.1007/s11548-017-1549-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 02/27/2017] [Indexed: 11/26/2022]
Abstract
PURPOSE For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes. METHODS The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation. RESULTS We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively. CONCLUSION We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.
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Affiliation(s)
- Takayuki Kitasaka
- School of Information Science, Aichi Institute of Technology, Yachikusa, Yakusa-cho, Toyota, 470-0392, Japan.
| | - Mitsuru Kagajo
- Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Yukitaka Nimura
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Yuichiro Hayashi
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Masahiro Oda
- Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Kazunari Misawa
- Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
| | - Kensaku Mori
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
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Wang X, Liu Y, Wu Z, Mou X, Zhou M, Ballester MAG, Zhang C. Automatic Labeling of Vascular Structures with Topological Constraints via HMM. LECTURE NOTES IN COMPUTER SCIENCE 2017. [DOI: 10.1007/978-3-319-66185-8_24] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Robben D, Türetken E, Sunaert S, Thijs V, Wilms G, Fua P, Maes F, Suetens P. Simultaneous segmentation and anatomical labeling of the cerebral vasculature. Med Image Anal 2016; 32:201-15. [DOI: 10.1016/j.media.2016.03.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Revised: 01/20/2016] [Accepted: 03/16/2016] [Indexed: 11/24/2022]
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From macro-scale to micro-scale computational anatomy: a perspective on the next 20 years. Med Image Anal 2016; 33:159-164. [PMID: 27423408 DOI: 10.1016/j.media.2016.06.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 06/23/2016] [Accepted: 06/27/2016] [Indexed: 11/23/2022]
Abstract
This paper gives our perspective on the next two decades of computational anatomy, which has made great strides in the recognition and understanding of human anatomy from conventional clinical images. The results from this field are now used in a variety of medical applications, including quantitative analysis of organ shapes, interventional assistance, surgical navigation, and population analysis. Several anatomical models have also been used in computational anatomy, and these mainly target millimeter-scale shapes. For example, liver-shape models are almost completely modeled at the millimeter scale, and shape variations are described at such scales. Most clinical 3D scanning devices have had just under 1 or 0.5 mm per voxel resolution for over 25 years, and this resolution has not changed drastically in that time. Although Z-axis (head-to-tail direction) resolution has been drastically improved by the introduction of multi-detector CT scanning devices, in-plane resolutions have not changed very much either. When we look at human anatomy, we can see different anatomical structures at different scales. For example, pulmonary blood vessels and lung lobes can be observed in millimeter-scale images. If we take 10-µm-scale images of a lung specimen, the alveoli and bronchiole regions can be located in them. Most work in millimeter-scale computational anatomy has been done by the medical-image analysis community. In the next two decades, we encourage our community to focus on micro-scale computational anatomy. In this perspective paper, we briefly review the achievements of computational anatomy and its impacts on clinical applications; furthermore, we show several possibilities from the viewpoint of microscopic computational anatomy by discussing experimental results from our recent research activities.
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Abstract
OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.
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15
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Automated extraction and labelling of the arterial tree from whole-body MRA data. Med Image Anal 2015; 24:28-40. [DOI: 10.1016/j.media.2015.05.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Revised: 05/09/2015] [Accepted: 05/13/2015] [Indexed: 11/18/2022]
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