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Ube T. Three-dimensional nanostructure analysis of non-stained Nafion in fuel-cell electrode by combined ADF-STEM tomography. Microscopy (Oxf) 2024; 73:318-328. [PMID: 38226523 PMCID: PMC11288185 DOI: 10.1093/jmicro/dfae002] [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: 08/23/2023] [Revised: 11/23/2023] [Accepted: 01/11/2024] [Indexed: 01/17/2024] Open
Abstract
The polymer electrolyte fuel cell (PEFC) is one of the strongest candidates for a next-generation power source for vehicles which do not emit CO2 gas as exhaust gas. The key factor in PEFCs is the nano-scaled electrochemical reactions that take place on the catalyst material and an ionomer supported by a carbon support. However, because the nano-scaled morphological features of the key materials in the catalyst compound cannot be observed clearly by transmission electron microscopy, improvement of PEFC performance had been approached by an imaginal schematic diagram based on an electrochemical analysis. In this study, we revealed the nano-scaled morphological features of the PEFC electrode in three dimensions and performed a quantitative analysis of the nanostructure by the newly developed 'Combined ADF-STEM tomography technique'. This method combines information from plural annular darkfield detectors with different electron collection angles and can emphasize the difference of the electron scattering intensity between the ionomer and carbon in the cross-sectional image of the reconstructed three-dimensional (3D) data. Therefore, this segmentation method utilizing image contrast does not require a high electron beam current like that used in energy dispersive X-ray analysis, and thus is suitable for electron beam damage-sensitive materials. By eliminating the process of manually determining the thresholds for obtaining classified component data from grayscale data, the obtained 3D structures have sufficient accuracy to allow quantitative analysis and specify the nano-scaled structural parameters directly related to power generation characteristics.
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Affiliation(s)
- Takuji Ube
- Nano-scale Characterization Center, JFE Techno-Research Corporation, Kawasaki-Ku, Kawasaki City, Kanagawa, Tokyo 2100855, Japan
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2
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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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Gulec SA, McGoron AJ. Radiomicrosphere Dosimetry: Principles and Current State of the Art. Semin Nucl Med 2022; 52:215-228. [DOI: 10.1053/j.semnuclmed.2021.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Chaichana A, Frey EC, Teyateeti A, Rhoongsittichai K, Tocharoenchai C, Pusuwan P, Jangpatarapongsa K. Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks. Med Phys 2021; 48:7877-7890. [PMID: 34657293 PMCID: PMC9298038 DOI: 10.1002/mp.15303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 08/10/2021] [Accepted: 10/11/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE 90 Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ-at-risks is labor-intensive and time-consuming in 90 Y SIRT planning. In this study, we developed a convolutional neural network (CNN)-based method for automated lungs, liver, and tumor segmentation on 99m Tc-MAA SPECT/CT images for 90 Y SIRT planning. METHODS 99m Tc-MAA SPECT/CT images and corresponding clinical segmentations were retrospectively collected from 56 patients who underwent 90 Y SIRT. The collected data were used to train three CNN-based segmentation algorithms for lungs, liver, and tumor segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), surface DSC, and average symmetric surface distance (ASSD). Dosimetric parameters (volume, counts, and lung shunt fraction) were measured from the segmentation results and were compared with clinical reference segmentations. RESULTS The evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97-0.98], 0.91 [0.83-0.93], and 0.85 [0.71-0.88]; surface DSCs of 0.99 [0.97-0.99], 0.86 [0.77-0.93], and 0.85 [0.62-0.93], and ASSDs of 0.91 [0.69-1.5], 4.8 [2.6-8.4], and 4.7 [3.5-9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX-2080Ti GPU. CONCLUSION This work presents CNN-based algorithms to segment lungs, liver, and tumor from 99m Tc-MAA SPECT/CT images. The results demonstrated the potential of the proposed CNN-based segmentation method for assisting 90 Y SIRT planning while drastically reducing operator time.
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Affiliation(s)
- Anucha Chaichana
- Department of Radiological Technology, Faculty of Medical TechnologyMahidol UniversityBangkok10700Thailand
| | - Eric C. Frey
- Johns Hopkins School of MedicineJohns Hopkins UniversityBaltimoreMaryland21218USA
- Radiopharmaceutical Imaging and Dosimetry, LLCLuthervilleMaryland21093USA
| | - Ajalaya Teyateeti
- Department of Radiology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Kijja Rhoongsittichai
- Department of Radiology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
| | - Chiraporn Tocharoenchai
- Department of Radiological Technology, Faculty of Medical TechnologyMahidol UniversityBangkok10700Thailand
| | - Pawana Pusuwan
- Department of Radiology, Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok10700Thailand
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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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Alirr OI. Deep learning and level set approach for liver and tumor segmentation from CT scans. J Appl Clin Med Phys 2020; 21:200-209. [PMID: 33113290 PMCID: PMC7592966 DOI: 10.1002/acm2.13003] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/11/2020] [Accepted: 07/12/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time-consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow-up assessment. METHOD This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region-based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region-based level aims to refine the predicted segmentation to find the correct final segmentation. RESULTS The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively. CONCLUSION The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.
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Affiliation(s)
- Omar Ibrahim Alirr
- College of Engineering and TechnologyAmerican University of the Middle EastKuwaitKuwait
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Winther H, Hundt C, Ringe KI, Wacker FK, Schmidt B, Jürgens J, Haimerl M, Beyer LP, Stroszczynski C, Wiggermann P, Verloh N. A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI. ROFO-FORTSCHR RONTG 2020; 193:305-314. [PMID: 32882724 DOI: 10.1055/a-1238-2887] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. MATERIALS AND METHODS Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. RESULTS Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and an overlap of 90.9 ± 4.9 %. A second human reader achieved a Sørensen-Dice coefficient of 95 % on a subset of the test set. CONCLUSION Our study introduces a fully automated liver volumetry scheme for Gd-EOB-DTPA-enhanced MR imaging. The neural network achieves competitive concordance with the ground truth regarding ICC, Sørensen-Dice, and overlap compared with manual segmentation. The neural network performs the task in just 60 seconds. KEY POINTS · The proposed neural network helps to segment the liver accurately, providing detailed information about patient-specific liver anatomy and volume.. · With the help of a deep learning-based neural network, fully automatic segmentation of the liver on MRI scans can be performed in seconds.. · A fully automatic segmentation scheme makes liver segmentation on MRI a valuable tool for treatment planning.. CITATION FORMAT · Winther H, Hundt C, Ringe KI et al. A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI. Fortschr Röntgenstr 2021; 193: 305 - 314.
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Affiliation(s)
- Hinrich Winther
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Christian Hundt
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Kristina Imeen Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Frank K Wacker
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Bertil Schmidt
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Julian Jürgens
- Department of Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael Haimerl
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | - Lukas Philipp Beyer
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | | | - Philipp Wiggermann
- Department of Radiology and Nuclear Medicine, Hospital Braunschweig, Germany
| | - Niklas Verloh
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
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Eslami M, Tabarestani S, Albarqouni S, Adeli E, Navab N, Adjouadi M. Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2553-2565. [PMID: 32078541 DOI: 10.1109/tmi.2020.2974159] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the wellestablished pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art al-gorithms along with ablation study and a demonstration video1 are provided to evaluate the efficacy and gauge the merits of the proposed approach.
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Nayak A, Baidya Kayal E, Arya M, Culli J, Krishan S, Agarwal S, Mehndiratta A. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. Int J Comput Assist Radiol Surg 2019; 14:1341-1352. [PMID: 31062266 DOI: 10.1007/s11548-019-01991-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 04/25/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE High mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach. METHODS Contrast-enhanced CT dataset of 40 patients (n = 40; M:F = 5:3; age = 25-55 years) with three groups of subjects: healthy (n = 14), cirrhosis (n = 12) and cirrhosis with HCC (n = 14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM). RESULTS The proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p < 0.05) among healthy and diseased liver, and using logistic regression, classification accuracy obtained was 92.5%. For detecting cirrhosis and HCC liver, SVM with RBF kernel obtained highest slice-wise and patient-wise prediction accuracy of 86.9% (precision = 0.93, recall = 0.7) and 80% (precision = 0.86, recall = 0.75), respectively, than that of linear kernel (slice-wise: accuracy = 85.4%, precision = 0.92, recall = 0.67; patient-wise: accuracy = 73.33%, precision = 0.75, recall = 0.75). CONCLUSIONS The proposed computer-aided diagnosis system for detecting cirrhosis and hepatocellular carcinoma (HCC) showed promising results and can be used as effective screening tool in medical image analysis.
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Affiliation(s)
- Akash Nayak
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India.,IBM Research, Bangalore, India
| | - Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Manish Arya
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Jayanth Culli
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Sonal Krishan
- Department of Radiology, Medanta The Medicity, Gurgaon, India
| | - Sumeet Agarwal
- Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India. .,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India.
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Jafargholi Rangraz E, Coudyzer W, Maleux G, Baete K, Deroose CM, Nuyts J. Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization. EJNMMI Res 2019; 9:19. [PMID: 30788640 PMCID: PMC6382918 DOI: 10.1186/s13550-019-0485-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 01/29/2019] [Indexed: 12/15/2022] Open
Abstract
Purpose We have developed a multi-modal imaging approach for SIRT, combining 99mTc-MAA SPECT/CT and/or 90Y PET, 18F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required. Method In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment 18F-FDG PET/CT, 99mTc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image. Result Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe). Conclusion This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients.
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Affiliation(s)
| | - Walter Coudyzer
- Radiology Section, Department of imaging and pathology, UZ Leuven, Leuven, Belgium
| | - Geert Maleux
- Radiology Section, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Kristof Baete
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Christophe M Deroose
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
| | - Johan Nuyts
- Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium
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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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Huang Q, Ding H, Wang X, Wang G. Fully automatic liver segmentation in CT images using modified graph cuts and feature detection. Comput Biol Med 2018. [DOI: 10.1016/j.compbiomed.2018.02.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Moghbel M, Mashohor S, Mahmud R, Saripan MIB. Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9550-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Moghbel M, Mashohor S, Mahmud R, Saripan MIB. Automatic liver segmentation on Computed Tomography using random walkers for treatment planning. EXCLI JOURNAL 2016; 15:500-517. [PMID: 28096782 PMCID: PMC5225683 DOI: 10.17179/excli2016-473] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2016] [Accepted: 07/13/2016] [Indexed: 11/10/2022]
Abstract
Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers. To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95 % and dice similarity coefficient of 0.91.
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Affiliation(s)
- Mehrdad Moghbel
- Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Syamsiah Mashohor
- Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Rozi Mahmud
- Cancer Resource & Education Center, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - M Iqbal Bin Saripan
- Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
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Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:9093721. [PMID: 27127536 PMCID: PMC4835633 DOI: 10.1155/2016/9093721] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 02/20/2016] [Accepted: 03/08/2016] [Indexed: 11/27/2022]
Abstract
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.
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Computerized Liver Segmentation from CT Images using Probabilistic Level Set Approach. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015. [DOI: 10.1007/s13369-015-1871-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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