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Alirr OI, Rahni AAA. Hepatic vessels segmentation using deep learning and preprocessing enhancement. J Appl Clin Med Phys 2023; 24:e13966. [PMID: 36933239 PMCID: PMC10161019 DOI: 10.1002/acm2.13966] [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: 07/28/2022] [Revised: 02/09/2023] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
PURPOSE Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. RESULTS The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. CONCLUSIONS The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.
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Affiliation(s)
- Omar Ibrahim Alirr
- College of Engineering and TechnologyAmerican University of the Middle EastEgailaKuwait
| | - Ashrani Aizzuddin Abd Rahni
- Department of ElectricalElectronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti KebangsaanBangiSelangorMalaysia
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Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med 2022; 151:106241. [PMID: 36379190 DOI: 10.1016/j.compbiomed.2022.106241] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
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Affiliation(s)
- Hao Li
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Yang Nan
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom.
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Hao W, Zhang J, Su J, Song Y, Liu Z, Liu Y, Qiu C, Han K. HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107003. [PMID: 35868034 DOI: 10.1016/j.cmpb.2022.107003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The segmentation and visualization of liver vessels in 3D CT images are essential for computer-aided diagnosis and preoperative planning of liver diseases. Due to the irregular structure of liver vessels and image noise, accurate extraction of liver vessels is difficult. In particular, accurate segmentation of small vessels is always a challenge, as multiple single down-sampling usually results in a loss of information. METHODS In this paper, we propose a hierarchical progressive multiscale learning network (HPM-Net) framework for liver vessel segmentation. Firstly, the hierarchical progressive multiscale learning network combines internal and external progressive learning methods to learn semantic information about liver vessels at different scales by acquiring receptive fields of different sizes. Secondly, to better capture vessel features, we propose a dual-branch progressive 3D Unet, which uses a dual-branch progressive (DBP) down-sampling strategy to reduce the loss of detailed information in the process of network down-sampling. Finally, a deep supervision mechanism is introduced into the framework and backbone network to speed up the network convergence and achieve better training of the network. RESULTS We conducted experiments on the public dataset 3Dircadb for liver vessel segmentation. The average dice coefficient and sensitivity of the proposed method reached 75.18% and 78.84%, respectively, both higher than the original network. CONCLUSION Experimental results show that the proposed hierarchical progressive multiscale network can accurately segment the labeled liver vessels from the CT images.
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Affiliation(s)
- Wen Hao
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Jing Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jun Su
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yuqing Song
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Zhe Liu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Yi Liu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chengjian Qiu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Kai Han
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
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Rahman H, Bukht TFN, Imran A, Tariq J, Tu S, Alzahrani A. A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet. Bioengineering (Basel) 2022; 9:bioengineering9080368. [PMID: 36004893 PMCID: PMC9404984 DOI: 10.3390/bioengineering9080368] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/25/2022] Open
Abstract
According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.
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Affiliation(s)
- Hameedur Rahman
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
| | | | - Azhar Imran
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
| | - Junaid Tariq
- Department of Computer Science, National University of Modern Languages (NUML), Rawalpindi Campus, Islamabad 44000, Pakistan
| | - Shanshan Tu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
- Correspondence:
| | - Abdulkareeem Alzahrani
- Computer Engineering and Science Department, Faculty of Computer Science and Information Technology, Al Baha University, Al Baha 65515, Saudi Arabia
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Altini N, Prencipe B, Cascarano GD, Brunetti A, Brunetti G, Triggiani V, Carnimeo L, Marino F, Guerriero A, Villani L, Scardapane A, Bevilacqua V. Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.157] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Ahmad M, Qadri SF, Ashraf MU, Subhi K, Khan S, Zareen SS, Qadri S. Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2665283. [PMID: 35634046 PMCID: PMC9132625 DOI: 10.1155/2022/2665283] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/06/2022] [Indexed: 12/11/2022]
Abstract
Segmentation of a liver in computed tomography (CT) images is an important step toward quantitative biomarkers for a computer-aided decision support system and precise medical diagnosis. To overcome the difficulties that come across the liver segmentation that are affected by fuzzy boundaries, stacked autoencoder (SAE) is applied to learn the most discriminative features of the liver among other tissues in abdominal images. In this paper, we propose a patch-based deep learning method for the segmentation of a liver from CT images using SAE. Unlike the traditional machine learning methods, instead of anticipating pixel by pixel learning, our algorithm utilizes the patches to learn the representations and identify the liver area. We preprocessed the whole dataset to get the enhanced images and converted each image into many overlapping patches. These patches are given as input to SAE for unsupervised feature learning. Finally, the learned features with labels of the images are fine tuned, and the classification is performed to develop the probability map in a supervised way. Experimental results demonstrate that our proposed algorithm shows satisfactory results on test images. Our method achieved a 96.47% dice similarity coefficient (DSC), which is better than other methods in the same domain.
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Affiliation(s)
- Mubashir Ahmad
- College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen, Guangdong Province 518060, China
- Department of Computer Science and IT, The University of Lahore, Sargodha Campus, 40100, Lahore, Pakistan
| | - Syed Furqan Qadri
- College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen, Guangdong Province 518060, China
| | - M. Usman Ashraf
- Department of Computer Science, GC Women University, Sialkot 51310, Pakistan
| | - Khalid Subhi
- Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Salabat Khan
- College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen, Guangdong Province 518060, China
| | - Syeda Shamaila Zareen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Salman Qadri
- Department of Computer Science, MNS University of Agriculture, Multan 60650, Pakistan
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Wang M, Jin R, Lu J, Song E, Ma G. Automatic CT liver Couinaud segmentation based on key bifurcation detection with attentive residual hourglass-based cascaded network. Comput Biol Med 2022; 144:105363. [DOI: 10.1016/j.compbiomed.2022.105363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/21/2022] [Accepted: 02/26/2022] [Indexed: 11/03/2022]
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8
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2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Abstract
Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels’ segmentation and liver segments’ classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments’ classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on “hard” samples, avoiding the gradient being overwhelmed by a large number of falsenegatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel’s probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments’ configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.
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Han X, Wu X, Wang S, Xu L, Xu H, Zheng D, Yu N, Hong Y, Yu Z, Yang D, Yang Z. Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network. Insights Imaging 2022; 13:26. [PMID: 35201517 PMCID: PMC8873293 DOI: 10.1186/s13244-022-01163-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/24/2022] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE We aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images. METHODS This retrospective study evaluated an automated method using a deep neural network that was trained, validated, and tested with 367, 157, and 158 portal venous phase MR images, respectively. The Dice similarity coefficient (DSC), mean surface distance (MSD), Hausdorff distance (HD), and volume ratio (RV) were used to quantitatively measure the accuracy of segmentation. The time consumed for model and manual segmentation was also compared. In addition, the model was applied to 100 consecutive cases from real clinical scenario for a qualitative evaluation and indirect evaluation. RESULTS In quantitative evaluation, the model achieved high accuracy for DSC, MSD, HD and RV (0.920, 3.34, 3.61 and 1.01, respectively). Compared to manual segmentation, the automated method reduced the segmentation time from 26 min to 8 s. In qualitative evaluation, the segmentation quality was rated as good in 79% of the cases, moderate in 15% and poor in 6%. In indirect evaluation, 93.4% (99/106) of lesions could be assigned to the correct segment by only referring to the results from automated segmentation. CONCLUSION The proposed model may serve as an effective tool for automated anatomical region annotation of the liver on MRI images.
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Affiliation(s)
- Xinjun Han
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xinru Wu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shuhui Wang
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Lixue Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Dandan Zheng
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Niange Yu
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Yanjie Hong
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Zhixuan Yu
- Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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11
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Guo B, Zhou F, Liu B, Bai X. Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images. Front Neurosci 2021; 15:756536. [PMID: 34899162 PMCID: PMC8660083 DOI: 10.3389/fnins.2021.756536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/13/2021] [Indexed: 11/13/2022] Open
Abstract
Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.
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Affiliation(s)
- Bin Guo
- Image Processing Center, School of Astronautics, Beihang University, Beijing, China
| | - Fugen Zhou
- Image Processing Center, School of Astronautics, Beihang University, Beijing, China
| | - Bo Liu
- Image Processing Center, School of Astronautics, Beihang University, Beijing, China
| | - Xiangzhi Bai
- Image Processing Center, School of Astronautics, Beihang University, Beijing, China
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Zhao C, Basu A. Pixel Distribution Learning for Vessel Segmentation under Multiple Scales. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2717-2721. [PMID: 34891812 DOI: 10.1109/embc46164.2021.9629614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this work we try to address if there is a better way to classify two distributions, rather than using histograms; and answer if we can make a deep learning network learn and classify distributions automatically. These improvements can have wide ranging applications in computer vision and medical image processing. More specifically, we propose a new vessel segmentation method based on pixel distribution learning under multiple scales. In particular, a spatial distribution descriptor named Random Permutation of Spatial Pixels (RPoSP) is derived from vessel images and used as the input to a convolutional neural network for distribution learning. Based on our preliminary experiments we currently believe that a wide network, rather than a deep one, is better for distribution learning. There is only one convolutional layer, one rectified linear layer and one fully connected layer followed by a softmax loss in our network. Furthermore, in order to improve the accuracy of the proposed approach, the RPoSP features are captured at multiple scales and combined together to form the input of the network. Evaluations using standard benchmark datasets demonstrate that the proposed approach achieves promising results compared to the state-of-the-art.
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Tooulias A, Tsoulfas G, Papadopoulos V, Alexiou M, Karolos IA, Pikridas C, Tsioukas V. Assisting Difficult Liver Operations Using 3D Printed Models. LIVERS 2021; 1:138-146. [DOI: 10.3390/livers1030013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/26/2024] Open
Abstract
Liver cancer is estimated to be the fifth most common in the world, while it is also considered the third leading cause of cancer death. In cases of primary liver cancer, surgery in combination with chemotherapy and radiotherapy can lead to a complete cure or significantly increase the patient’s life expectancy. Since the liver is an organ that performs several critical functions in the human body, the precise estimation of the disease (position and size of tumors and its vicinity to vessels) plays a vital role in a successful operation. In some cases, the removal of the tumor may be successful, but the percentage of the hepatic remnant may not be sufficient to sustain life. Therefore, accurate imaging of the tumor of the liver and proper planning of a difficult surgery to remove tumor(s) from a patient’s liver can be a lifesaver and lead to a complete cure of the disease. The aim of the present study is the initial accurate representation of the liver (parenchyma, tumors, vessels) as a digital three-dimensional (3D) model using advanced image processing and machine learning techniques and its 3D printing in 1:1 scale representing the full size of the liver with the tumor(s). A model of this type has been used at our University surgical department to plan complex hepatobiliary surgeries, provide more accurate information to the patients and their families, as well as improve the training of medical students and resident surgeons and fellows.
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Affiliation(s)
- Andreas Tooulias
- 1st Surgical Department, School of Medicine, Aristotle University of Thessaloniki, 56403 Thessaloniki, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, School of Medicine, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece
| | - Vasileios Papadopoulos
- 1st Surgical Department, School of Medicine, Aristotle University of Thessaloniki, 56403 Thessaloniki, Greece
| | - Maria Alexiou
- 1st Surgical Department, School of Medicine, Aristotle University of Thessaloniki, 56403 Thessaloniki, Greece
| | - Ion-Anastasios Karolos
- Department of Geodesy and Surveying, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Christos Pikridas
- Department of Geodesy and Surveying, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Vassilios Tsioukas
- Department of Geodesy and Surveying, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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Rahimi A, Khalil A, Faisal A, Lai KW. CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-Based Registration. Curr Med Imaging 2021; 18:61-66. [PMID: 34433403 DOI: 10.2174/1573405617666210825155659] [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: 01/04/2021] [Revised: 05/17/2021] [Accepted: 06/01/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Early diagnosis of liver cancer may increase life expectancy. Computed tomography (CT) and magnetic resonance imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. OBJECTIVE The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. METHODS In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel(R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. RESULTS The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. CONCLUSION The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
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Affiliation(s)
- Aisyah Rahimi
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan. Malaysia
| | - Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan. Malaysia
| | - Amir Faisal
- Biomedical Engineering, Institut Teknologi Sumatera, Lampung Selatan, 35365. Indonesia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur. Malaysia
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Alirr OI, Rahni AAA. Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 2021; 33:304-323. [PMID: 31428898 DOI: 10.1007/s10278-019-00262-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
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Affiliation(s)
- Omar Ibrahim Alirr
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
| | - Ashrani Aizzuddin Abd Rahni
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
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Mardani K, Maghooli K. Enhancing retinal blood vessel segmentation in medical images using combined segmentation modes extracted by DBSCAN and morphological reconstruction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102837] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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17
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Nazir A, Cheema MN, Sheng B, Li P, Kim J, Lee TY. Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning. IEEE Trans Biomed Eng 2021; 68:2540-2551. [PMID: 33417536 DOI: 10.1109/tbme.2021.3050310] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model. Our CIL architecture is able to find optimal solutions, which we use to update the model with liver vessel CTA images. A novel ternary tree based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies. The tree topologies of the recipient's and donor's liver vessels are then used for an appropriate matching. The proposed algorithm utilizes a set of defined vessel tree variants which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL. We introduce a novel concept of in-order digital string based comparison to match the geometry of two anatomically varied trees. Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to state-of-the-art.
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18
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Yang J, Fu M, Hu Y. Liver vessel segmentation based on inter-scale V-Net. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4327-4340. [PMID: 34198439 DOI: 10.3934/mbe.2021217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.
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Affiliation(s)
- Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education Northeastern University, Shenyang 110000, China
| | - Meihan Fu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
| | - Ying Hu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
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Ciecholewski M, Kassjański M. Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. SENSORS 2021; 21:s21062027. [PMID: 33809361 PMCID: PMC7999381 DOI: 10.3390/s21062027] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/07/2021] [Accepted: 03/09/2021] [Indexed: 12/12/2022]
Abstract
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.
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20
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Hussain R, Lalande A, Girum KB, Guigou C, Bozorg Grayeli A. Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network. Sci Rep 2021; 11:4406. [PMID: 33623074 PMCID: PMC7902630 DOI: 10.1038/s41598-021-83955-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 02/10/2021] [Indexed: 01/22/2023] Open
Abstract
Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.
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Affiliation(s)
- Raabid Hussain
- ImViA Laboratory, University of Burgundy Franche Comte, Dijon, France.
| | - Alain Lalande
- ImViA Laboratory, University of Burgundy Franche Comte, Dijon, France.,Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | | | - Caroline Guigou
- ImViA Laboratory, University of Burgundy Franche Comte, Dijon, France.,Otolaryngology Department, University Hospital of Dijon, Dijon, France
| | - Alexis Bozorg Grayeli
- ImViA Laboratory, University of Burgundy Franche Comte, Dijon, France.,Otolaryngology Department, University Hospital of Dijon, Dijon, France
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Kosieradzki M, Lisik W, Gierwiało R, Sitnik R. Applicability of Augmented Reality in an Organ Transplantation. Ann Transplant 2020; 25:e923597. [PMID: 32732862 PMCID: PMC7418780 DOI: 10.12659/aot.923597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 02/20/2020] [Indexed: 12/20/2022] Open
Abstract
Augmented reality (AR) delivers virtual information or some of its elements to the real world. This technology, which has been used primarily for entertainment and military applications, has vigorously entered medicine, especially in radiology and surgery, yet has never been used in organ transplantation. AR could be useful in training transplant surgeons, promoting organ donations, graft retrieval and allocation, and microscopic diagnosis of rejection, treatment of complications, and post-transplantation neoplasms. The availability of AR display tools such as Smartphone screens and head-mounted goggles, accessibility of software for automated image segmentation and 3-dimensional reconstruction, and algorithms allowing registration, make augmented reality an attractive tool for surgery including transplantation. The shortage of hospital IT specialists and insufficient investments from medical equipment manufacturers into the development of AR technology remain the most significant obstacles in its broader application.
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Affiliation(s)
- Maciej Kosieradzki
- Department of General and Transplantation Surgery, The Medical University of Warsaw, Warsaw, Poland
| | - Wojciech Lisik
- Department of General and Transplantation Surgery, The Medical University of Warsaw, Warsaw, Poland
| | - Radosław Gierwiało
- Virtual Reality Techniques Division, Institute of Micromechanics and Photonics, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
| | - Robert Sitnik
- Virtual Reality Techniques Division, Institute of Micromechanics and Photonics, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
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Ni J, Wu J, Tong J, Chen Z, Zhao J. GC-Net: Global context network for medical image segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 190:105121. [PMID: 31623863 DOI: 10.1016/j.cmpb.2019.105121] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/23/2019] [Accepted: 10/04/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Medical image segmentation plays an important role in many clinical applications such as disease diagnosis, surgery planning, and computer-assisted therapy. However, it is a very challenging task due to variant images qualities, complex shapes of objects, and the existence of outliers. Recently, researchers have presented deep learning methods to segment medical images. However, these methods often use the high-level features of the convolutional neural network directly or the high-level features combined with the shallow features, thus ignoring the role of the global context features for the segmentation task. Consequently, they have limited capability on extensive medical segmentation tasks. The purpose of this work is to devise a neural network with global context feature information for accomplishing medical image segmentation of different tasks. METHODS The proposed global context network (GC-Net) consists of two components; feature encoding and decoding modules. We use multiple convolutions and batch normalization layers in the encoding module. On the other hand, the decoding module is formed by a proposed global context attention (GCA) block and squeeze and excitation pyramid pooling (SEPP) block. The GCA module connects low-level and high-level features to produce more representative features, while the SEPP module increases the size of the receptive field and the ability of multi-scale feature fusion. Moreover, a weighted cross entropy loss is designed to better balance the segmented and non-segmented regions. RESULTS The proposed GC-Net is validated on three publicly available datasets and one local dataset. The tested medical segmentation tasks include segmentation of intracranial blood vessel, retinal vessels, cell contours, and lung. Experiments demonstrate that, our network outperforms state-of-the-art methods concerning several commonly used evaluation metrics. CONCLUSION Medical segmentation of different tasks can be accurately and effectively achieved by devising a deep convolutional neural network with a global context attention mechanism.
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Affiliation(s)
- Jiajia Ni
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China; College of Internet of Things Engineering, HoHai University Changzhou, China
| | - Jianhuang Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China.
| | - Jing Tong
- College of Internet of Things Engineering, HoHai University Changzhou, China
| | - Zhengming Chen
- College of Internet of Things Engineering, HoHai University Changzhou, China
| | - Junping Zhao
- Institute of Medical Informatics, Chinese PLA General Hospital, China
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Liu Y, Qiao L, Hao X, Lu H, Duan Y, Dong Q. Variations of the right branch of hepatic portal vein in children based on three-dimensional simulation technology. Surg Radiol Anat 2020; 42:1467-1473. [PMID: 32424682 DOI: 10.1007/s00276-020-02499-3] [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: 01/16/2020] [Accepted: 05/08/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To study the variations of the right branch of the hepatic portal vein in children. METHODS A total of 810 children's abdominal CT images were reconstructed with three-dimensional (3D) simulation software, Variations of the right branch of the hepatic portal vein were analyzed and classified. RESULTS The most common anatomy (type A) was seen in 355 patients (43.83%). Trifurcation in the right anterior portal vein (type B) variation was seen in 250 cases (30.86%). The right posterior portal vein arched without obvious branching (type C) was seen in 71 cases (8.77%). There were 134 special variants (16.54%) named type D, including 14 cases (1.73%) with the right anterior branch in four sub-branches, 13 cases (1.60%) in one trunk and multiple sub-branches, 92 cases (11.36%) originating from the left trunk of the portal vein, and 15 cases (1.85%) with the VI segment of the portal vein originating from the right anterior branch of the portal vein. CONCLUSION Variations in the right branch of the hepatic portal vein seems to be very frequent. Recognition of such variations is important in the preoperative evaluation of children with surgery planned, because these variations may have implications for anatomy-guided liver resection and for planning the operative approach.
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Affiliation(s)
- Yusheng Liu
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Lingyan Qiao
- Department of Pediatric Endocrinology and Genetic Metabolic Diseases, Qingdao Women and Children's Hospital, Qingdao, 266000, China
| | - Xiwei Hao
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Hongting Lu
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Yuhe Duan
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Qian Dong
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
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Zhang H, Bai P, Min X, Liu Q, Ren Y, Li H, Li Y. Hepatic vessel segmentation based on animproved 3D region growing algorithm. ACTA ACUST UNITED AC 2020. [DOI: 10.1088/1742-6596/1486/3/032038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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26
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Cheng YL, Ma MN, Zhang LJ, Jin CJ, Ma L, Zhou Y. Retinal blood vessel segmentation based on Densely Connected U-Net. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3088-3108. [PMID: 32987518 DOI: 10.3934/mbe.2020175] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The segmentation of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper proposes a new architecture of the U-Net network for retinal blood vessel segmentation. Adding dense block to U-Net network makes each layer's input come from the all previous layer's output which improves the segmentation accuracy of small blood vessels. The effectiveness of the proposed method has been evaluated on two public datasets (DRIVE and CHASE_DB1). The obtained results (DRIVE: Acc = 0.9559, AUC = 0.9793, CHASE_DB1: Acc = 0.9488, AUC = 0.9785) demonstrate the better performance of the proposed method compared to the state-of-the-art methods. Also, the results show that our method achieves better results for the segmentation of small blood vessels and can be helpful to evaluate related ophthalmic diseases.
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Affiliation(s)
- Yin Lin Cheng
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510006, China
| | - Meng Nan Ma
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510006, China
| | - Liang Jun Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Chen Jin Jin
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510006, China
| | - Li Ma
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510006, China
| | - Yi Zhou
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510006, China
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Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning. Int J Comput Assist Radiol Surg 2019; 15:239-248. [PMID: 31617057 DOI: 10.1007/s11548-019-02078-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 10/02/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment. METHOD The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments. RESULTS The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy. CONCLUSION Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.
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Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, Magnin B. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme. Comput Biol Med 2019; 110:42-51. [PMID: 31121506 DOI: 10.1016/j.compbiomed.2019.04.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/18/2019] [Accepted: 04/18/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task. METHODS We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations. RESULTS We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. CONCLUSIONS We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.
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Affiliation(s)
- Marie-Ange Lebre
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France.
| | - Antoine Vacavant
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Manuel Grand-Brochier
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Hugo Rositi
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Armand Abergel
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Pascal Chabrot
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Benoît Magnin
- Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F-63000, Clermont-Ferrand, France
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Ahmad M, Ai D, Xie G, Qadri SF, Song H, Huang Y, Wang Y, Yang J. Deep Belief Network Modeling for Automatic Liver Segmentation. IEEE ACCESS 2019; 7:20585-20595. [DOI: 10.1109/access.2019.2896961] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:2376317. [PMID: 30510670 PMCID: PMC6231381 DOI: 10.1155/2018/2376317] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 09/22/2018] [Accepted: 10/04/2018] [Indexed: 01/04/2023]
Abstract
In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.
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An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J Comput Assist Radiol Surg 2018; 13:1169-1176. [PMID: 29860549 DOI: 10.1007/s11548-018-1801-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 05/22/2018] [Indexed: 10/14/2022]
Abstract
PURPOSE Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities. METHODS In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries. RESULTS The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets. CONCLUSIONS The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.
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Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:71-91. [PMID: 29544791 DOI: 10.1016/j.cmpb.2018.02.001] [Citation(s) in RCA: 226] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/23/2017] [Accepted: 02/02/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Yang X, Yang JD, Hwang HP, Yu HC, Ahn S, Kim BW, You H. Segmentation of liver and vessels from CT images and classification of liver segments for preoperative liver surgical planning in living donor liver transplantation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:41-52. [PMID: 29544789 DOI: 10.1016/j.cmpb.2017.12.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 11/13/2017] [Accepted: 12/11/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The present study developed an effective surgical planning method consisting of a liver extraction stage, a vessel extraction stage, and a liver segment classification stage based on abdominal computerized tomography (CT) images. METHODS An automatic seed point identification method, customized level set methods, and an automated thresholding method were applied in this study to extraction of the liver, portal vein (PV), and hepatic vein (HV) from CT images. Then, a semi-automatic method was developed to separate PV and HV. Lastly, a local searching method was proposed for identification of PV branches and the nearest neighbor approximation method was applied to classifying liver segments. RESULTS Onsite evaluation of liver segmentation provided by the SLIVER07 website showed that the liver segmentation method achieved an average volumetric overlap accuracy of 95.2%. An expert radiologist evaluation of vessel segmentation showed no false positive errors or misconnections between PV and HV in the extracted vessel trees. Clinical evaluation of liver segment classification using 43 CT datasets from two medical centers showed that the proposed method achieved high accuracy in liver graft volumetry (absolute error, AE = 45.2 ± 20.9 ml; percentage of AE, %AE = 6.8% ± 3.2%; percentage of %AE > 10% = 16.3%; percentage of %AE > 20% = none) and the classified segment boundaries agreed with the intraoperative surgical cutting boundaries by visual inspection. CONCLUSIONS The method in this study is effective in segmentation of liver and vessels and classification of liver segments and can be applied to preoperative liver surgical planning in living donor liver transplantation.
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Affiliation(s)
- Xiaopeng Yang
- Department of Industrial Management and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Jae Do Yang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Hong Pil Hwang
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Hee Chul Yu
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea.
| | - Sungwoo Ahn
- Department of Surgery, Chonbuk National University Medical School, Jeonju, 54907, South Korea; Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk University Hospital, Jeonju, 54907, South Korea; Research Institute for Endocrine Sciences, Chonbuk National University, Jeonju, 54907, South Korea
| | - Bong-Wan Kim
- Department of Liver Transplantation and Hepatobiliary Surgery, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Heecheon You
- Department of Industrial Management and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
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Ibragimov B, Toesca D, Chang D, Koong A, Xing L. Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol 2017; 62:8943-8958. [PMID: 28994665 DOI: 10.1088/1361-6560/aa9262] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was [Formula: see text] 0.83 and [Formula: see text] 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNNs and anatomical analysis can be used for the accurate segmentation of the PV and potentially integrated into liver radiation therapy planning.
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Affiliation(s)
- Bulat Ibragimov
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Palo Alto, CA 94305, United States of America
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Kong X, Nie L, Zhang H, Wang Z, Ye Q, Tang L, Huang W, Li J. Do 3D Printing Models Improve Anatomical Teaching About Hepatic Segments to Medical Students? A Randomized Controlled Study. World J Surg 2017; 40:1969-76. [PMID: 27172803 DOI: 10.1007/s00268-016-3541-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND It is a difficult and frustrating task for young surgeons and medical students to understand the anatomy of hepatic segments. We tried to develop an optimal 3D printing model of hepatic segments as a teaching aid to improve the teaching of hepatic segments. METHODS A fresh human cadaveric liver without hepatic disease was CT scanned. After 3D reconstruction, three types of 3D computer models of hepatic structures were designed and 3D printed as models of hepatic segments without parenchyma (type 1) and with transparent parenchyma (type 2), and hepatic ducts with segmental partitions (type 3). These models were evaluated by six experts using a five-point Likert scale. Ninety two medical freshmen were randomized into four groups to learn hepatic segments with the aid of the three types of models and traditional anatomic atlas (TAA). Their results of two quizzes were compared to evaluate the teaching effects of the four methods. RESULTS Three types of models were successful produced which displayed the structures of hepatic segments. By experts' evaluation, type 3 model was better than type 1 and 2 models in anatomical condition, type 2 and 3 models were better than type 1 model in tactility, and type 3 model was better than type 1 model in overall satisfaction (P < 0.05). The first quiz revealed that type 1 model was better than type 2 model and TAA, while type 3 model was better than type 2 and TAA in teaching effects (P < 0.05). The second quiz found that type 1 model was better than TAA, while type 3 model was better than type 2 model and TAA regarding teaching effects (P < 0.05). Only TAA group had significant declines between two quizzes (P < 0.05). CONCLUSIONS The model with segmental partitions proves to be optimal, because it can best improve anatomical teaching about hepatic segments.
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Affiliation(s)
- Xiangxue Kong
- Department of Anatomy, Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Lanying Nie
- Department of Traumatic Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Huijian Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhanglin Wang
- Department of Anatomy, Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Ye
- Department of Radiology, The 3rd Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Lei Tang
- Department of Anatomy, Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Wenhua Huang
- Department of Anatomy, Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong, China.
| | - Jianyi Li
- Department of Anatomy, Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong, China.
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Wang X, Zheng Y, Gan L, Wang X, Sang X, Kong X, Zhao J. Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM). PLoS One 2017; 12:e0185249. [PMID: 28981530 PMCID: PMC5628825 DOI: 10.1371/journal.pone.0185249] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 09/09/2017] [Indexed: 11/19/2022] Open
Abstract
This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.
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Affiliation(s)
- Xuehu Wang
- School of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- * E-mail:
| | - Lan Gan
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Xuan Wang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinting Sang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangfeng Kong
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Zhao
- School of Electronic and Information Engineering, Hebei University, Baoding, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China
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Goceri E, Shah ZK, Gurcan MN. Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2811. [PMID: 27315322 DOI: 10.1002/cnm.2811] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 06/10/2016] [Accepted: 06/12/2016] [Indexed: 06/06/2023]
Abstract
The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Evgin Goceri
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Zarine K Shah
- Department of Radiology, Wexner Medical Center, The Ohio State University, Columbus, OH, USA
| | - Metin N Gurcan
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
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Correia MM, Jesus JPD, Feitosa R, Oliveira DA. The introduction of navigation in liver surgery in Brazil. Rev Col Bras Cir 2016; 41:451-4. [PMID: 25742413 DOI: 10.1590/0100-69912014006012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2013] [Accepted: 01/20/2014] [Indexed: 12/29/2022] Open
Abstract
The authors thoroughly report the development, the technical aspects and the performance of the first navigated liver resections, by laparotomy and laparoscopy, in Brazil, done at the National Cancer Institute, Ministry of Health, using a surgical navigator.
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Affiliation(s)
- Mauro Monteiro Correia
- José Alencar Gomes da Silva National Cancer Institute, Ministry of Health, Rio de Janeiro, Brazil
| | - José Paulo de Jesus
- José Alencar Gomes da Silva National Cancer Institute, Ministry of Health, Rio de Janeiro, Brazil
| | - Raul Feitosa
- Pontifical Catholic University of Rio de Janeiro
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Alirr OI, Rahni AAA. Development of automatic segmentation of the inferior vena cava in abdominal CT scans. 2016 IEEE EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES) 2016. [DOI: 10.1109/iecbes.2016.7843449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Hu P, Wu F, Peng J, Liang P, Kong D. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 2016; 61:8676-8698. [PMID: 27880735 DOI: 10.1088/1361-6560/61/24/8676] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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41
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Kong X, Nie L, Zhang H, Wang Z, Ye Q, Tang L, Li J, Huang W. Do Three-dimensional Visualization and Three-dimensional Printing Improve Hepatic Segment Anatomy Teaching? A Randomized Controlled Study. JOURNAL OF SURGICAL EDUCATION 2016; 73:264-269. [PMID: 26868314 DOI: 10.1016/j.jsurg.2015.10.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 09/25/2015] [Accepted: 10/02/2015] [Indexed: 06/05/2023]
Abstract
INTRODUCTION Hepatic segment anatomy is difficult for medical students to learn. Three-dimensional visualization (3DV) is a useful tool in anatomy teaching, but current models do not capture haptic qualities. However, three-dimensional printing (3DP) can produce highly accurate complex physical models. Therefore, in this study we aimed to develop a novel 3DP hepatic segment model and compare the teaching effectiveness of a 3DV model, a 3DP model, and a traditional anatomical atlas. MATERIALS AND METHODS A healthy candidate (female, 50-years old) was recruited and scanned with computed tomography. After three-dimensional (3D) reconstruction, the computed 3D images of the hepatic structures were obtained. The parenchyma model was divided into 8 hepatic segments to produce the 3DV hepatic segment model. The computed 3DP model was designed by removing the surrounding parenchyma and leaving the segmental partitions. Then, 6 experts evaluated the 3DV and 3DP models using a 5-point Likert scale. A randomized controlled trial was conducted to evaluate the educational effectiveness of these models compared with that of the traditional anatomical atlas. RESULTS The 3DP model successfully displayed the hepatic segment structures with partitions. All experts agreed or strongly agreed that the 3D models provided good realism for anatomical instruction, with no significant differences between the 3DV and 3DP models in each index (p > 0.05). Additionally, the teaching effects show that the 3DV and 3DP models were significantly better than traditional anatomical atlas in the first and second examinations (p < 0.05). Between the first and second examinations, only the traditional method group had significant declines (p < 0.05). CONCLUSION A novel 3DP hepatic segment model was successfully developed. Both the 3DV and 3DP models could improve anatomy teaching significantly.
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Affiliation(s)
- Xiangxue Kong
- Department of Anatomy, Guangdong Province Key Laboratory of Medical Biomechanics, School of Basic Medicine Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Lanying Nie
- Department of Traumatic Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Huijian Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhanglin Wang
- Department of Anatomy, Guangdong Province Key Laboratory of Medical Biomechanics, School of Basic Medicine Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiang Ye
- Department of Radiology, The 3rd Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Lei Tang
- Department of Anatomy, Guangdong Province Key Laboratory of Medical Biomechanics, School of Basic Medicine Science, Southern Medical University, Guangzhou, Guangdong, China
| | - Jianyi Li
- Department of Anatomy, Guangdong Province Key Laboratory of Medical Biomechanics, School of Basic Medicine Science, Southern Medical University, Guangzhou, Guangdong, China.
| | - Wenhua Huang
- Department of Anatomy, Guangdong Province Key Laboratory of Medical Biomechanics, School of Basic Medicine Science, Southern Medical University, Guangzhou, Guangdong, China.
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Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg 2015; 11:817-26. [PMID: 26646416 DOI: 10.1007/s11548-015-1332-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 11/19/2015] [Indexed: 12/11/2022]
Abstract
PURPOSE Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. METHODS First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. RESULTS Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. CONCLUSION The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.
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Luu HM, Klink C, Moelker A, Niessen W, van Walsum T. Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images. Phys Med Biol 2015; 60:3905-26. [PMID: 25909487 DOI: 10.1088/0031-9155/60/10/3905] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p < 0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.
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Affiliation(s)
- Ha Manh Luu
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
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Adaptive Mesh Expansion Model (AMEM) for liver segmentation from CT image. PLoS One 2015; 10:e0118064. [PMID: 25769030 PMCID: PMC4358832 DOI: 10.1371/journal.pone.0118064] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Accepted: 01/03/2015] [Indexed: 01/22/2023] Open
Abstract
This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.
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Luo S, Li X, Li J. Review on the Methods of Automatic Liver Segmentation from Abdominal Images. ACTA ACUST UNITED AC 2014. [DOI: 10.4236/jcc.2014.22001] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Wang G, Zhang S, Li F, Gu L. A new segmentation framework based on sparse shape composition in liver surgery planning system. Med Phys 2013; 40:051913. [PMID: 23635283 DOI: 10.1118/1.4802215] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system. METHODS The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer. RESULTS Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively. CONCLUSIONS The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.
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Affiliation(s)
- Guotai Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
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Luo Q, Qin W, Wen T, Gu J, Gaio N, Chen S, Li L, Xie Y. Segmentation of abdomen MR images using kernel graph cuts with shape priors. Biomed Eng Online 2013; 12:124. [PMID: 24295198 PMCID: PMC4220691 DOI: 10.1186/1475-925x-12-124] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Accepted: 11/08/2013] [Indexed: 12/03/2022] Open
Abstract
Background Abdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of similar objects close to each other. Method In this study, a novel method for tissue or organ segmentation in abdomen MR imaging is proposed; this method combines kernel graph cuts (KGC) with shape priors. First, the region growing algorithm and morphology operations are used to obtain the initial contour. Second, shape priors are obtained by training the shape templates, which were collected from different human subjects with kernel principle component analysis (KPCA) after the registration between all the shape templates and the initial contour. Finally, a new model is constructed by integrating the shape priors into the kernel graph cuts energy function. The entire process aims to obtain an accurate image segmentation. Results The proposed segmentation method has been applied to abdominal organs MR images. The results showed that a satisfying segmentation without boundary leakage and segmentation incorrect can be obtained also in presence of similar tissues. Quantitative experiments were conducted for comparing the proposed segmentation with other three methods: DRLSE, initial erosion contour and KGC without shape priors. The comparison is based on two quantitative performance measurements: the probabilistic rand index (PRI) and the variation of information (VoI). The proposed method has the highest PRI value (0.9912, 0.9983 and 0.9980 for liver, right kidney and left kidney respectively) and the lowest VoI values (1.6193, 0.3205 and 0.3217 for liver, right kidney and left kidney respectively). Conclusion The proposed method can overcome boundary leakage. Moreover it can segment liver and kidneys in abdominal MR images without segmentation errors due to the presence of similar tissues. The shape priors based on KPCA was integrated into fully automatic graph cuts algorithm (KGC) to make the segmentation algorithm become more robust and accurate. Furthermore, if a shelter is placed onto the target boundary, the proposed method can still obtain satisfying segmentation results.
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Affiliation(s)
| | | | | | - Jia Gu
- The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P, R, China.
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Real-time image guidance in laparoscopic liver surgery: first clinical experience with a guidance system based on intraoperative CT imaging. Surg Endosc 2013; 28:933-40. [PMID: 24178862 DOI: 10.1007/s00464-013-3249-0] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Accepted: 10/04/2013] [Indexed: 01/11/2023]
Abstract
BACKGROUND Laparoscopic liver surgery is particularly challenging owing to restricted access, risk of bleeding, and lack of haptic feedback. Navigation systems have the potential to improve information on the exact position of intrahepatic tumors, and thus facilitate oncological resection. This study aims to evaluate the feasibility of a commercially available augmented reality (AR) guidance system employing intraoperative robotic C-arm cone-beam computed tomography (CBCT) for laparoscopic liver surgery. METHODS A human liver-like phantom with 16 target fiducials was used to evaluate the Syngo iPilot(®) AR system. Subsequently, the system was used for the laparoscopic resection of a hepatocellular carcinoma in segment 7 of a 50-year-old male patient. RESULTS In the phantom experiment, the AR system showed a mean target registration error of 0.96 ± 0.52 mm, with a maximum error of 2.49 mm. The patient successfully underwent the operation and showed no postoperative complications. CONCLUSION The use of intraoperative CBCT and AR for laparoscopic liver resection is feasible and could be considered an option for future liver surgery in complex cases.
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Sakaguchi T, Suzuki S, Hiraide T, Shibasaki Y, Morita Y, Suzuki A, Fukumoto K, Inaba K, Takehara Y, Nasu H, Kamiya M, Yamashita S, Ushio T, Konno H. Detection of intrahepatic veno-venous shunts by three-dimensional venography using multidetector-row computed tomography during angiography. Surg Today 2013; 44:662-7. [PMID: 23975592 DOI: 10.1007/s00595-013-0710-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 02/07/2013] [Indexed: 10/26/2022]
Abstract
PURPOSE The hepatic vein (HV) can be removed during hepatectomy if there is an effective intrahepatic veno-venous shunt (vv-shunt). We evaluated the efficacy of vv-shunt detection by three-dimensional (3D) venography reconstructed from multidetector-row computed tomography (MDCT) during angiography. METHODS 3D venography was reconstructed using computer software in 88 patients with intrahepatic tumors. RESULTS We found that 12 patients had one shunt [4 right hepatic vein (RHV)-middle hepatic vein (MHV) and 12 RHV- inferior right hepatic vein (IRHV)] and 1 patient had 2 shunts (RHV-MHV and -IRHV), confirming a clinically efficient vv-shunt in 14.8% of the patients. In one patient with an RHV-IRHV shunt, the preserved RHV-IRHV shunt worked well and prevented congestion of the postero-caudal subsegment after central bisegmentectomy with partial resection of the RHV ventral trunk for huge hepatocellular carcinoma (HCC). CONCLUSIONS Although the vv-shunt detection rate by 3D venography is low, a visualized vv-shunt proved to be efficient. Thus, invasive occlusion venography is avoidable if a vv-shunt is seen on 3D venography.
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Affiliation(s)
- Takanori Sakaguchi
- Second Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, 431-3192, Japan,
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Drechsler K, Oyarzun Laura C, Wesarg S. Interventional planning of liver resections: an overview. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:3744-7. [PMID: 23366742 DOI: 10.1109/embc.2012.6346781] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Liver cancer is the third most common type of cancer. Among available treatment options, a surgical resection offers the best prognosis for long-term survival. It is important that such a surgical procedure is carefully prepared. Modern computer technology offers convenient ways to simulate different resection scenarios and help to determine the best treatment for a given case. This paper provides a non-exhaustive overview of existing computer-based systems for interventional planning of liver resections. They are reviewed according to their medical use case, e.g. if they support typical or atypical resections.
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Affiliation(s)
- Klaus Drechsler
- Department of Cognitive Computing & Medical Imaging, Fraunhofer Institute for Computer Graphics Research (IGD), 64283 Darmstadt, Germany.
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