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Wang L, Fatemi M, Alizad A. Artificial intelligence techniques in liver cancer. Front Oncol 2024; 14:1415859. [PMID: 39290245 PMCID: PMC11405163 DOI: 10.3389/fonc.2024.1415859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/15/2024] [Indexed: 09/19/2024] Open
Abstract
Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.
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Affiliation(s)
- Lulu Wang
- Department of Engineering, School of Technology, Reykjavık University, Reykjavík, Iceland
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
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Lai J, Luo Z, Liu J, Hu H, Jiang H, Liu P, He L, Cheng W, Ren W, Wu Y, Piao JG, Wu Z. Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma. NANO LETTERS 2024; 24:10614-10623. [PMID: 39046153 PMCID: PMC11363118 DOI: 10.1021/acs.nanolett.4c02823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/19/2024] [Accepted: 07/22/2024] [Indexed: 07/25/2024]
Abstract
Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.
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Affiliation(s)
- Jianjun Lai
- Department
of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China
- Instiute
of Intelligent Control and Robotics, Hangzhou
Dianzi University, Hangzhou 310018, China
| | - Zhizeng Luo
- Instiute
of Intelligent Control and Robotics, Hangzhou
Dianzi University, Hangzhou 310018, China
| | - Jiping Liu
- Department
of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Haili Hu
- Department
of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China
| | - Hao Jiang
- Department
of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China
| | - Pengyuan Liu
- Department
of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China
| | - Li He
- School
of Pharmaceutical Sciences, Zhejiang Chinese
Medical University, Hangzhou 310053, China
| | - Weiyi Cheng
- School
of Pharmaceutical Sciences, Zhejiang Chinese
Medical University, Hangzhou 310053, China
| | - Weiye Ren
- School
of Pharmaceutical Sciences, Zhejiang Chinese
Medical University, Hangzhou 310053, China
| | - Yajun Wu
- Department
of Pharmacy, Zhejiang Hospital, Hangzhou 310013, China
| | - Ji-Gang Piao
- School
of Pharmaceutical Sciences, Zhejiang Chinese
Medical University, Hangzhou 310053, China
| | - Zhibing Wu
- Department
of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China
- Department
of Radiation Oncology, Affiliated Zhejiang
Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China
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Zhang B, Qiu S, Liang T. Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images. Bioengineering (Basel) 2024; 11:737. [PMID: 39061819 PMCID: PMC11273630 DOI: 10.3390/bioengineering11070737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet.
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Affiliation(s)
- Benyue Zhang
- Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China;
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100408, China
| | - Shi Qiu
- Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China;
| | - Ting Liang
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710119, China
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Lin H, Zhao M, Zhu L, Pei X, Wu H, Zhang L, Li Y. Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy. Front Oncol 2024; 14:1423774. [PMID: 38966060 PMCID: PMC11222586 DOI: 10.3389/fonc.2024.1423774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/06/2024] [Indexed: 07/06/2024] Open
Abstract
Purpose Addressing the challenges of unclear tumor boundaries and the confusion between cysts and tumors in liver tumor segmentation, this study aims to develop an auto-segmentation method utilizing Gaussian filter with the nnUNet architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation. Methods Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours. Results The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation. Conclusion Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.
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Affiliation(s)
- Hongyu Lin
- Department of Oncology, First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Min Zhao
- Department of Oncology, First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Lingling Zhu
- Department of Oncology, First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xi Pei
- Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China
| | - Haotian Wu
- Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China
| | - Lian Zhang
- Department of Oncology, First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ying Li
- Department of Oncology, First Hospital of Hebei Medical University, Shijiazhuang, China
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Dwivedi K, Sharkey M, Alabed S, Langlotz CP, Swift AJ, Bluethgen C. External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT. Eur Radiol 2024; 34:2727-2737. [PMID: 37775589 PMCID: PMC10957646 DOI: 10.1007/s00330-023-10235-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/25/2023] [Accepted: 07/24/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). METHODS This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. RESULTS A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. CONCLUSION State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. CLINICAL RELEVANCE Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. KEY POINTS • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.
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Affiliation(s)
- Krit Dwivedi
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK.
- Academic Department of Radiology, Royal Hallamshire Hospital, Glossop Road, Sheffield, S10 2JF, USA.
| | - Michael Sharkey
- 3DLab, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
| | - Curtis P Langlotz
- Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA
| | - Andy J Swift
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
| | - Christian Bluethgen
- Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA
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Zhang W, Zhao N, Gao Y, Huang B, Wang L, Zhou X, Li Z. Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats. Magn Reson Imaging 2024; 107:1-7. [PMID: 38147969 DOI: 10.1016/j.mri.2023.12.006] [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: 10/16/2022] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images. MATERIALS AND METHODS In this prospective study, animal models of liver fibrosis were induced by injecting subcutaneously a mixture of Carbon tetrachloride and olive oil. A total of 99 male Wistar rats were successfully induced and underwent MR scanning with no contrast agent to get T1-weighted images. The regions of interest (ROIs) of the whole liver were delineated layer by layer along the liver edge by 3D Slicer. For segmentation task, all T1-weighted images were randomly divided into training and test cohorts in a ratio of 7:3. For classification, images containing the hepatic maximum diameter of every rat were selected and 80% images of no liver fibrosis (NLF), early liver fibrosis (ELF) and progressive liver fibrosis (PLF) stages were randomly selected for training, while the rest were used for testing. Liver segmentation was performed by the nnU-Net model. The convolutional neural network (CNN) was used for classification task of liver fibrosis stages. The Dice similarity coefficient was used to evaluate the segmentation performance of nnU-Net. Confusion matrix, ROC curve and accuracy were used to show the classification performance of CNN. RESULTS A total of 2628 images were obtained from 99 Wistar rats by MR scanning. For liver segmentation by nnU-Net, the Dice similarity coefficient in the test set was 0.8477. The accuracies of CNN in staging NLF, ELF and PLF were 0.73, 0.89 and 0.84, respectively. The AUCs were 0.76, 0.88 and 0.79, respectively. CONCLUSION The nnU-Net architecture is of high accuracy for liver segmentation and CNN for assessment of liver fibrosis with T1-weighted images.
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Affiliation(s)
- Wenjing Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nan Zhao
- College of Computer Science and Technology of Qingdao University, Qingdao, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Baoxiang Huang
- College of Computer Science and Technology of Qingdao University, Qingdao, China
| | - Lili Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Lin C, Guo Y, Huang X, Rao S, Zhou J. Esophageal cancer detection via non-contrast CT and deep learning. Front Med (Lausanne) 2024; 11:1356752. [PMID: 38510455 PMCID: PMC10953501 DOI: 10.3389/fmed.2024.1356752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 01/29/2024] [Indexed: 03/22/2024] Open
Abstract
Background Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images. Methods In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted. Results In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test). Conclusion The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.
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Affiliation(s)
- Chong Lin
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, Fujian, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, Fujian, China
| | - Yi Guo
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, Fujian, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, Fujian, China
| | - Xu Huang
- Departments of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Shanghai, Fujian, China
- Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, Fujian, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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G S, Appadurai JP, Kavin BP, C K, Lai WC. En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis. Biomedicines 2023; 11:biomedicines11051309. [PMID: 37238979 DOI: 10.3390/biomedicines11051309] [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: 02/09/2023] [Revised: 03/23/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder-Decoder Network (En-DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly.
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Affiliation(s)
- Suganeshwari G
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
| | - Jothi Prabha Appadurai
- Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India
| | - Balasubramanian Prabhu Kavin
- Department of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Chengalpattu District, Kattankulathur 603203, Tamilnadu, India
| | - Kavitha C
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119, Tamil Nadu, India
| | - Wen-Cheng Lai
- Bachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
- Department Electronic Engineering, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
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Vladimirov N, Brui E, Levchuk A, Al-Haidri W, Fokin V, Efimtcev A, Bendahan D. CNN-based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures. Magn Reson Med 2023; 90:737-751. [PMID: 37094028 DOI: 10.1002/mrm.29671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE Automatic measurement of wrist cartilage volume in MR images. METHODS We assessed the performance of four manually optimized variants of the U-Net architecture, nnU-Net and Mask R-CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch-based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross-validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed. RESULTS The U-Net-based networks outperformed the patch-based CNN in terms of segmentation homogeneity and quality, while Mask R-CNN did not show an acceptable performance. The median 3D DSC value computed with the U-Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U-Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U-Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI-based wrist cartilage volume is strongly affected by the image resolution. CONCLUSIONS U-Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine-tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
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Affiliation(s)
- Nikita Vladimirov
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
| | - Ekaterina Brui
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
| | - Anatoliy Levchuk
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
- Department of Radiology, Federal Almazov North-West Medical Research Center, Saint-Petersburg, Russia
| | - Walid Al-Haidri
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
| | - Vladimir Fokin
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
- Department of Radiology, Federal Almazov North-West Medical Research Center, Saint-Petersburg, Russia
| | - Aleksandr Efimtcev
- School of Physics and Engineering, ITMO University, Saint-Petersburg, Russia
- Department of Radiology, Federal Almazov North-West Medical Research Center, Saint-Petersburg, Russia
| | - David Bendahan
- Centre de Résonance Magnétique Biologique et Médicale, Aix-Marseille Universite, CNRS, Marseille, France
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