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Zhang W, Tao Y, Huang Z, Li Y, Chen Y, Song T, Ma X, Zhang Y. Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5735-5761. [PMID: 38872556 DOI: 10.3934/mbe.2024253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
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Affiliation(s)
- Wencong Zhang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Yuxi Tao
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhanyao Huang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Yue Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yingjia Chen
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Tengfei Song
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xiangyuan Ma
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Yaqin Zhang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
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Shetty K, Birkhold A, Jaganathan S, Strobel N, Egger B, Kowarschik M, Maier A. BOSS: Bones, organs and skin shape model. Comput Biol Med 2023; 165:107383. [PMID: 37657357 DOI: 10.1016/j.compbiomed.2023.107383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/21/2023] [Accepted: 08/14/2023] [Indexed: 09/03/2023]
Abstract
A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.
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Affiliation(s)
- Karthik Shetty
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91058, Germany; Siemens Healthcare GmbH, Forchheim, 91301, Germany.
| | | | - Srikrishna Jaganathan
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91058, Germany; Siemens Healthcare GmbH, Forchheim, 91301, Germany
| | - Norbert Strobel
- Siemens Healthcare GmbH, Forchheim, 91301, Germany; Institute of Medical Engineering Schweinfurt, Technical University of Applied Sciences Würzburg-Schweinfurt, Schweinfurt, 97421, Germany
| | - Bernhard Egger
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91058, Germany
| | | | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91058, Germany
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Moslehi S, Foruzan AH, Chen YW, Hu H. Characterisation of focal liver lesions in multi-phase CT images using textural and pathological descriptors. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2156390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Saeed Moslehi
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran
| | - Amir Hossein Foruzan
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran
| | - Yen-Wei Chen
- Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang, China
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Jin R, Wang M, Xu L, Lu J, Song E, Ma G. Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour. Med Phys 2022; 50:2100-2120. [PMID: 36413182 DOI: 10.1002/mp.16116] [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: 10/20/2022] [Revised: 10/27/2022] [Accepted: 11/05/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods. METHODS The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues. RESULTS The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, - 0.02 % $-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively. CONCLUSIONS The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.
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Affiliation(s)
- Renchao Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Manyang Wang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lijun Xu
- School of Computer and Information Engineering, Hubei University, Wuhan, China
| | - Jiayi Lu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guangzhi Ma
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Czipczer V, Manno-Kovacs A. Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
There is increasing interest in video object detection for many situations, such as industrial processes, surveillance systems, and nature exploration. In this work, we were concerned with the detection of pedestrians in video sequences. The aim was to deal with issues associated with the background, scale, contrast, or resolution of the video frames, which cause inaccurate detection of pedestrians. The proposed method was based on the combination of two techniques: motion detection by background subtraction (MDBS) and active shape models (ASM). The MDBS technique aids in the identification of a moving region of interest in the video sequence, which potentially includes a pedestrian; then, the ASM algorithm actively finds and adjusts the silhouette of the pedestrian. We tested the proposed MDBS + ASM method with video sequences from open repositories, and the results were favorable in scenes where pedestrians were in a well-illuminated environment. The mean fit error was up to 4.5 pixels. In contrast, in scenes where reflections, occlusions, or pronounced movement are present, the identification was slightly affected; the mean fit error was 8.3 pixels in the worst case. The main contribution of this work was exploring the potential of the combination of MDBS and ASM for performance improvements in the contour-based detection of a moving pedestrian walking in a controlled environment. We present a straightforward method based on classical algorithms which have been proven effective for pedestrian detection. In addition, since we were looking for a practical process that could work in real-time applications (for example, closed-circuit television video or surveillance systems), we established our approach with simple techniques.
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Mohagheghi S, Foruzan AH. Developing an explainable deep learning boundary correction method by incorporating cascaded x-Dim models to improve segmentation defects in liver CT images. Comput Biol Med 2022; 140:105106. [PMID: 34864581 DOI: 10.1016/j.compbiomed.2021.105106] [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: 08/15/2021] [Revised: 11/16/2021] [Accepted: 11/29/2021] [Indexed: 11/21/2022]
Abstract
Deep learning methods achieved remarkable results in medical image analysis tasks but it has not yet been widely used by medical professionals. One of the main reasons for this restricted usage is the uncertainty of the reasons that influence the decision of the model. Explainable AI methods have been developed to improve the transparency, interpretability, and explainability of the black-box AI methods. The result of an explainable segmentation method will be more trusted by experts. In this study, we designed an explainable deep correction method by incorporating cascaded 1D and 2D models to refine the output of other models and provide reliable yet accurate results. We implemented a 2-step loop with a 1D local boundary validation model in the first step, and a 2D image patch segmentation model in the second step, to refine incorrect segmented regions slice-by-slice. The proposed method improved the result of the CNN segmentation models and achieved state-of-the-art results on 3D liver segmentation with the average dice coefficient of 98.27 on the Sliver07 dataset.
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Affiliation(s)
- Saeed Mohagheghi
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran.
| | - Amir Hossein Foruzan
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran.
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Araújo JDL, da Cruz LB, Diniz JOB, Ferreira JL, Silva AC, de Paiva AC, Gattass M. Liver segmentation from computed tomography images using cascade deep learning. Comput Biol Med 2022; 140:105095. [PMID: 34902610 DOI: 10.1016/j.compbiomed.2021.105095] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/17/2021] [Accepted: 11/27/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Liver segmentation is a fundamental step in the treatment planning and diagnosis of liver cancer. However, manual segmentation of liver is time-consuming because of the large slice quantity and subjectiveness associated with the specialist's experience, which can lead to segmentation errors. Thus, the segmentation process can be automated using computational methods for better time efficiency and accuracy. However, automatic liver segmentation is a challenging task, as the liver can vary in shape, ill-defined borders, and lesions, which affect its appearance. We aim to propose an automatic method for liver segmentation using computed tomography (CT) images. METHODS The proposed method, based on deep convolutional neural network models and image processing techniques, comprise of four main steps: (1) image preprocessing, (2) initial segmentation, (3) reconstruction, and (4) final segmentation. RESULTS We evaluated the proposed method using 131 CT images from the LiTS image base. An average sensitivity of 95.45%, an average specificity of 99.86%, an average Dice coefficient of 95.64%, an average volumetric overlap error (VOE) of 8.28%, an average relative volume difference (RVD) of -0.41%, and an average Hausdorff distance (HD) of 26.60 mm were achieved. CONCLUSIONS This study demonstrates that liver segmentation, even when lesions are present in CT images, can be efficiently performed using a cascade approach and including a reconstruction step based on deep convolutional neural networks.
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Affiliation(s)
- José Denes Lima Araújo
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Luana Batista da Cruz
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - João Otávio Bandeira Diniz
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil; Federal Institute of Maranhão, BR-226, SN, Campus Grajaú, Vila Nova, 65 940-000, Grajaú, MA, Brazil.
| | - Jonnison Lima Ferreira
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil; Federal Institute of Amazonas, Rua Santos Dumont, SN, Campus Tabatinga, Vila Verde, 69 640-000, Tabatinga, AM, Brazil.
| | - Aristófanes Corrêa Silva
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Anselmo Cardoso de Paiva
- Applied Computing Group (NCA - UFMA), Federal University of Maranhão, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65 085-580, São Luís, MA, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro, R. São Vicente, 225, Gávea, 22 453-900, Rio de Janeiro, RJ, Brazil.
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Wang X, Zhang Z, Wu K, Yin X, Guo H. Gabor Dictionary of Sparse Image Patches Selected in Prior Boundaries for 3D Liver Segmentation in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5552864. [PMID: 34925736 PMCID: PMC8677387 DOI: 10.1155/2021/5552864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/26/2021] [Accepted: 10/04/2021] [Indexed: 11/17/2022]
Abstract
The gray contrast between the liver and other soft tissues is low, and the boundary is not obvious. As a result, it is still a challenging task to accurately segment the liver from CT images. In recent years, methods of machine learning have become a research hotspot in the field of medical image segmentation because they can effectively use the "gold standard" personalized features of the liver from different data. However, machine learning usually requires a large number of data samples to train the model and improve the accuracy of medical image segmentation. This paper proposed a method for liver segmentation based on the Gabor dictionary of sparse image blocks with prior boundaries. This method reduced the number of samples by selecting the test sample set within the initial boundary area of the liver. The Gabor feature was extracted and the query dictionary was created, and the sparse coefficient was calculated to obtain the boundary information of the liver. By optimizing the reconstruction error and filling holes, a smooth liver boundary was obtained. The proposed method was tested on the MICCAI 2007 dataset and ISBI2017 dataset, and five measures were used to evaluate the results. The proposed method was compared with methods for liver segmentation proposed in recent years. The experimental results show that this method can improve the accuracy of liver segmentation and effectively repair the discontinuity and local overlap of segmentation results.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
| | - Zhiling Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
| | - Kunlun Wu
- Hebei Research Institute of Construction and Geotechnical Investigation Co.,Ltd., Shijiazhuang, Hebei, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Haifeng Guo
- College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
- Research Center of Machine Vision Engineering and Technology of Hebei Province, Baoding 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China
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Shi C, Xian M, Zhou X, Wang H, Cheng HD. Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation. Med Image Anal 2021; 73:102152. [PMID: 34280669 DOI: 10.1016/j.media.2021.102152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 06/02/2021] [Accepted: 06/27/2021] [Indexed: 12/24/2022]
Abstract
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images. Firstly, we propose a multi-slice LRTD scheme to recover the underlying low-rank structure embedded in 3D medical images. It performs the LRTD on small image segments consisting of multiple consecutive image slices. Then, we present an LRTD-based atlas construction method to generate tumor-free liver atlases that mitigates the performance degradation of liver segmentation due to the presence of tumors. Finally, we introduce an LRTD-based MAS algorithm to derive patient-specific liver atlases for each test image, and to achieve accurate pairwise image registration and label propagation. Extensive experiments on three public databases of pathological liver cases validate the effectiveness of the proposed method. Both qualitative and quantitative results demonstrate that, in the presence of major pathology, the proposed method is more accurate and robust than state-of-the-art methods.
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Affiliation(s)
- Changfa Shi
- Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China; Department of Computer Science, Utah State University, Logan, UT 84322, USA
| | - Min Xian
- Department of Computer Science, University of Idaho, Idaho Falls, ID 83402, USA.
| | - Xiancheng Zhou
- Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China
| | - Haotian Wang
- Department of Computer Science, University of Idaho, Idaho Falls, ID 83402, USA
| | - Heng-Da Cheng
- Department of Computer Science, Utah State University, Logan, UT 84322, USA
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Xu Z, Tao B, Liu C, Han D, Zhang J, Liu J, Li S, Li W, Wang J, Liang J, Cao F. Three-dimensional quantitative assessment of myocardial infarction via multimodality fusion imaging: methodology, validation, and preliminary clinical application. Quant Imaging Med Surg 2021; 11:3175-3189. [PMID: 34249644 PMCID: PMC8250027 DOI: 10.21037/qims-20-702] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 02/24/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND The precise assessment of myocardial infarction (MI) is crucial both for therapeutic interventions in old MI and the development of new and effective techniques to repair injured myocardium. A novel method was developed to assess left ventricular (LV) quantitatively infarction through three-dimensional (3D) multimodality fusion based on computed tomography angiography (CTA) and technetium-99m methoxyisobutylisonitrile (99mTc-MIBI) single-photon emission computed tomography (SPECT) images. This study sought to develop a 3D quantitative method for MI for pre-clinical study and clinical application. METHODS Three months after the MI models were established in 20 minipigs, CTA and SPECT images were acquired separately, which were then aligned automatically with the constraints of the shape and the whole heart and LV myocardium position. Infarct ratios were quantified based on the 3D fusion images. The quantitative assessment was then experimentally validated via an ex vivo histology analysis using triphenyl-tetrazolium-chloride staining and subsequently applied to post-MI patients (n=8). RESULTS The location of an infarct identified by the SPECT was consistent with that identified by an ex vivo heart in a 3D space. Infarct size determined by CTA-SPECT was correlated with infarct size assessed by triphenyl-tetrazolium-chloride pathology {27.6% [interquartile range (IQR) 17.1-34.7%] vs. 24.1% (IQR 14.7-32.5%), r2=0.99, P<0.01}. In clinical cases, the CTA-SPECT 3D fusion quantitative results were significantly correlated with the quantitative perfusion SPECT results (r=0.976, P<0.01). CONCLUSIONS The proposed 3D fusion quantitative assessment method provides reliable and intuitive evaluations of infarction. This novel quantification technique enables whole heart quantification for the pre-operation evaluation and post-diagnosis management of old MI patients. It could also be applied to the design of 3D-printed cardiac patches.
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Affiliation(s)
- Zhenzhen Xu
- School of Life Science and Technology, Xidian University, Xi’an, China
| | - Bo Tao
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, Hainan Hospital of Chinese PLA General Hospital, Sanya, China
| | - Chuanbin Liu
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Dong Han
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jibin Zhang
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Junsong Liu
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Sulei Li
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Weijie Li
- Department of Cardiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Jing Wang
- Department of Nuclear Medicine, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi’an, China
| | - Feng Cao
- Department of Geriatric Cardiology, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
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Jimenez-Pastor A, Alberich-Bayarri A, Lopez-Gonzalez R, Marti-Aguado D, França M, Bachmann RSM, Mazzucco J, Marti-Bonmati L. Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images. Eur Radiol 2021; 31:7876-7887. [PMID: 33768292 DOI: 10.1007/s00330-021-07838-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/08/2021] [Accepted: 02/25/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To automate the segmentation of whole liver parenchyma on multi-echo chemical shift encoded (MECSE) MR examinations using convolutional neural networks (CNNs) to seamlessly quantify precise organ-related imaging biomarkers such as the fat fraction and iron load. METHODS A retrospective multicenter collection of 183 MECSE liver MR examinations was conducted. An encoder-decoder CNN was trained (107 studies) following a 5-fold cross-validation strategy to improve the model performance and ensure lack of overfitting. Proton density fat fraction (PDFF) and R2* were quantified on both manual and CNN segmentation masks. Different metrics were used to evaluate the CNN performance over both unseen internal (46 studies) and external (29 studies) validation datasets to analyze reproducibility. RESULTS The internal test showed excellent results for the automatic segmentation with a dice coefficient (DC) of 0.93 ± 0.03 and high correlation between the quantification done with the predicted mask and the manual segmentation (rPDFF = 1 and rR2* = 1; p values < 0.001). The external validation was also excellent with a different vendor but the same magnetic field strength, proving the generalization of the model to other manufacturers with DC of 0.94 ± 0.02. Results were lower for the 1.5-T MR same vendor scanner with DC of 0.87 ± 0.06. Both external validations showed high correlation in the quantification (rPDFF = 1 and rR2* = 1; p values < 0.001). In both internal and external validation datasets, the relative error for the PDFF and R2* quantification was below 4% and 1% respectively. CONCLUSION Liver parenchyma can be accurately segmented with CNN in a vendor-neutral virtual approach, allowing to obtain reproducible automatic whole organ virtual biopsies. KEY POINTS • Whole liver parenchyma can be automatically segmented using convolutional neural networks. • Deep learning allows the creation of automatic pipelines for the precise quantification of liver-related imaging biomarkers such as PDFF and R2*. • MR "virtual biopsy" can become a fast and automatic procedure for the assessment of chronic diffuse liver diseases in clinical practice.
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Affiliation(s)
- Ana Jimenez-Pastor
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Aragon Avenue, 30, 13th floor, Office J, 46021, Valencia, Spain.
| | - Angel Alberich-Bayarri
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Aragon Avenue, 30, 13th floor, Office J, 46021, Valencia, Spain
| | - Rafael Lopez-Gonzalez
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Aragon Avenue, 30, 13th floor, Office J, 46021, Valencia, Spain
| | - David Marti-Aguado
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain
| | - Manuela França
- Radiology Department, Centro Hospitalar Universitário do Porto (CHUP), Porto, Portugal
| | | | | | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230-PREBI) at La Fe Health Research Institute, and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain.,Radiology Department, La Fe University and Polytechnic Hospital, Valencia, Spain
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13
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Abstract
In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.
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Affiliation(s)
- Wei-Yen Hsu
- Department of Information Management
- Advanced Institute of Manufacturing with High-Tech Innovations
- Center for Innovative Research on Aging Society, National Chung Cheng University, Chiayi
| | - Chih-Cheng Lu
- Department of Information Management
- Division of Urology, Department of Surgery, Chi Mei Medical Center, Liouying, Tainan, Taiwan
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14
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Zhang Y, Zheng X, Xue Q. A Deep Neural Network Based Glottal Flow Model for Predicting Fluid-Structure Interactions during Voice Production. APPLIED SCIENCES (BASEL, SWITZERLAND) 2020; 10:705. [PMID: 34306737 DOI: 10.3390/app10113794] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
This paper proposes a machine-learning based reduced-order model that can provide fast and accurate prediction of the glottal flow during voice production. The model is based on the Bernoulli equation with a viscous loss term predicted by a deep neural network (DNN) model. The training data of the DNN model is a Navier-Stokes (N-S) equation-based three-dimensional simulation of glottal flows in various glottal shapes generated by a synthetic shape function, which can be obtained by superimposing the instantaneous modal displacements during vibration on the prephonatory geometry of the glottal shape. The input parameters of the DNN model are the geometric and flow parameters extracted from discretized cross sections of the glottal shapes and the output target is the corresponding flow resistance coefficient. With this trained DNN-Bernoulli model, the flow resistance coefficient as well as the flow rate and pressure distribution in any given glottal shape generated by the synthetic shape function can be predicted. The model is further coupled with a finite-element method based solid dynamics solver for simulating fluid-structure interactions (FSI). The prediction performance of the model for both static shape and FSI simulations is evaluated by comparing the solutions to those obtained by the Bernoulli and N-S model. The model shows a good prediction performance in accuracy and efficiency, suggesting a promise for future clinical use.
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Affiliation(s)
- Yang Zhang
- Department of Mechanical Engineering, University of Maine, Orono, ME 04469, USA
| | - Xudong Zheng
- Department of Mechanical Engineering, University of Maine, Orono, ME 04469, USA
| | - Qian Xue
- Department of Mechanical Engineering, University of Maine, Orono, ME 04469, USA
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15
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Zhang Y, Niu L. A Substructure Segmentation Method of Left Heart Regions from Cardiac CT Images Using Local Mesh Descriptors, Context and Spatial Location Information. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s105466181902010x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Lu X, Xie Q, Zha Y, Wang D. Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images. Sci Rep 2018; 8:10700. [PMID: 30013150 PMCID: PMC6048104 DOI: 10.1038/s41598-018-28787-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 06/22/2018] [Indexed: 11/09/2022] Open
Abstract
Liver segmentation is an essential procedure in computer-assisted surgery, radiotherapy, and volume measurement. It is still a challenging task to extract liver tissue from 3D CT images owing to nearby organs with similar intensities. In this paper, an automatic approach integrating multi-dimensional features into graph cut refinement is developed and validated. Multi-atlas segmentation is utilized to estimate the coarse shape of liver on the target image. The unsigned distance field based on initial shape is then calculated throughout the whole image, which aims at automatic graph construction during refinement procedure. Finally, multi-dimensional features and shape constraints are embedded into graph cut framework. The optimal liver region can be precisely detected with a minimal cost. The proposed technique is evaluated on 40 CT scans, obtained from two public databases Sliver07 and 3Dircadb1. The dataset Sliver07 is considered as the training set for parameter learning. On the dataset 3Dircadb1, the average of volume overlap is up to 94%. The experiment results indicate that the proposed method has ability to reach the desired boundary of liver and has potential value for clinical application.
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Affiliation(s)
- Xuesong Lu
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, P. R. China
| | - Qinlan Xie
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, P. R. China
| | - Yunfei Zha
- Department of Radiology, Remin Hospital of Wuhan University, Wuhan, 430060, P. R. China
| | - Defeng Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China. .,School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China. .,Research Centre for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
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17
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An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:6506049. [PMID: 29201332 PMCID: PMC5672701 DOI: 10.1155/2017/6506049] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 04/23/2017] [Indexed: 11/18/2022]
Abstract
Random walk (RW) method has been widely used to segment the organ in the volumetric medical image. However, it leads to a very large-scale graph due to a number of nodes equal to a voxel number and inaccurate segmentation because of the unavailability of appropriate initial seed point setting. In addition, the classical RW algorithm was designed for a user to mark a few pixels with an arbitrary number of labels, regardless of the intensity and shape information of the organ. Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner. Our strategy is to employ the previous segmented slice to obtain the shape and intensity knowledge of the target organ for the adjacent slice. According to the prior knowledge, the object/background seed points can be dynamically updated for the adjacent slice by combining the narrow band threshold (NBT) method and the organ model with a Gaussian process. Finally, a high-quality image segmentation result can be automatically achieved using Bayes RW algorithm. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for liver segmentation (p < 0.001).
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18
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Dura E, Domingo J, Göçeri E, Martí-Bonmatí L. A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction. Pattern Anal Appl 2017. [DOI: 10.1007/s10044-017-0666-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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