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Han M, Luo X, Xie X, Liao W, Zhang S, Song T, Wang G, Zhang S. DMSPS: Dynamically mixed soft pseudo-label supervision for scribble-supervised medical image segmentation. Med Image Anal 2024; 97:103274. [PMID: 39043109 DOI: 10.1016/j.media.2024.103274] [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: 12/18/2023] [Revised: 05/11/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024]
Abstract
High performance of deep learning on medical image segmentation rely on large-scale pixel-level dense annotations, which poses a substantial burden on medical experts due to the laborious and time-consuming annotation process, particularly for 3D images. To reduce the labeling cost as well as maintain relatively satisfactory segmentation performance, weakly-supervised learning with sparse labels has attained increasing attentions. In this work, we present a scribble-based framework for medical image segmentation, called Dynamically Mixed Soft Pseudo-label Supervision (DMSPS). Concretely, we extend a backbone with an auxiliary decoder to form a dual-branch network to enhance the feature capture capability of the shared encoder. Considering that most pixels do not have labels and hard pseudo-labels tend to be over-confident to result in poor segmentation, we propose to use soft pseudo-labels generated by dynamically mixing the decoders' predictions as auxiliary supervision. To further enhance the model's performance, we adopt a two-stage approach where the sparse scribbles are expanded based on predictions with low uncertainties from the first-stage model, leading to more annotated pixels to train the second-stage model. Experiments on ACDC dataset for cardiac structure segmentation, WORD dataset for 3D abdominal organ segmentation and BraTS2020 dataset for 3D brain tumor segmentation showed that: (1) compared with the baseline, our method improved the average DSC from 50.46% to 89.51%, from 75.46% to 87.56% and from 52.61% to 76.53% on the three datasets, respectively; (2) DMSPS achieved better performance than five state-of-the-art scribble-supervised segmentation methods, and is generalizable to different segmentation backbones. The code is available online at: https://github.com/HiLab-git/DMSPS.
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Affiliation(s)
- Meng Han
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangde Luo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Xiangjiang Xie
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Wenjun Liao
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Chengdu, China; School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shichuan Zhang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Chengdu, China
| | - Tao Song
- SenseTime Research, Shanghai, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
| | - Shaoting Zhang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
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2
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Qu Y, Lu T, Zhang S, Wang G. ScribSD+: Scribble-supervised medical image segmentation based on simultaneous multi-scale knowledge distillation and class-wise contrastive regularization. Comput Med Imaging Graph 2024; 116:102416. [PMID: 39018640 DOI: 10.1016/j.compmedimag.2024.102416] [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: 01/15/2024] [Revised: 06/16/2024] [Accepted: 07/04/2024] [Indexed: 07/19/2024]
Abstract
Despite that deep learning has achieved state-of-the-art performance for automatic medical image segmentation, it often requires a large amount of pixel-level manual annotations for training. Obtaining these high-quality annotations is time-consuming and requires specialized knowledge, which hinders the widespread application that relies on such annotations to train a model with good segmentation performance. Using scribble annotations can substantially reduce the annotation cost, but often leads to poor segmentation performance due to insufficient supervision. In this work, we propose a novel framework named as ScribSD+ that is based on multi-scale knowledge distillation and class-wise contrastive regularization for learning from scribble annotations. For a student network supervised by scribbles and the teacher based on Exponential Moving Average (EMA), we first introduce multi-scale prediction-level Knowledge Distillation (KD) that leverages soft predictions of the teacher network to supervise the student at multiple scales, and then propose class-wise contrastive regularization which encourages feature similarity within the same class and dissimilarity across different classes, thereby effectively improving the segmentation performance of the student network. Experimental results on the ACDC dataset for heart structure segmentation and a fetal MRI dataset for placenta and fetal brain segmentation demonstrate that our method significantly improves the student's performance and outperforms five state-of-the-art scribble-supervised learning methods. Consequently, the method has a potential for reducing the annotation cost in developing deep learning models for clinical diagnosis.
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Affiliation(s)
- Yijie Qu
- University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Lu
- Sichuan Provincial People's Hospital, Chengdu, China
| | - Shaoting Zhang
- University of Electronic Science and Technology of China, Chengdu, China; Shanghai AI lab, Shanghai, China
| | - Guotai Wang
- University of Electronic Science and Technology of China, Chengdu, China; Shanghai AI lab, Shanghai, China.
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3
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Wang W, Mao Q, Tian Y, Zhang Y, Xiang Z, Ren L. FMD-UNet: fine-grained feature squeeze and multiscale cascade dilated semantic aggregation dual-decoder UNet for COVID-19 lung infection segmentation from CT images. Biomed Phys Eng Express 2024; 10:055031. [PMID: 39142295 DOI: 10.1088/2057-1976/ad6f12] [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: 04/05/2024] [Accepted: 08/14/2024] [Indexed: 08/16/2024]
Abstract
With the advancement of computer-aided diagnosis, the automatic segmentation of COVID-19 infection areas holds great promise for assisting in the timely diagnosis and recovery of patients in clinical practice. Currently, methods relying on U-Net face challenges in effectively utilizing fine-grained semantic information from input images and bridging the semantic gap between the encoder and decoder. To address these issues, we propose an FMD-UNet dual-decoder U-Net network for COVID-19 infection segmentation, which integrates a Fine-grained Feature Squeezing (FGFS) decoder and a Multi-scale Dilated Semantic Aggregation (MDSA) decoder. The FGFS decoder produces fine feature maps through the compression of fine-grained features and a weighted attention mechanism, guiding the model to capture detailed semantic information. The MDSA decoder consists of three hierarchical MDSA modules designed for different stages of input information. These modules progressively fuse different scales of dilated convolutions to process the shallow and deep semantic information from the encoder, and use the extracted feature information to bridge the semantic gaps at various stages, this design captures extensive contextual information while decoding and predicting segmentation, thereby suppressing the increase in model parameters. To better validate the robustness and generalizability of the FMD-UNet, we conducted comprehensive performance evaluations and ablation experiments on three public datasets, and achieved leading Dice Similarity Coefficient (DSC) scores of 84.76, 78.56 and 61.99% in COVID-19 infection segmentation, respectively. Compared to previous methods, the FMD-UNet has fewer parameters and shorter inference time, which also demonstrates its competitiveness.
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Affiliation(s)
- Wenfeng Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Qi Mao
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Yi Tian
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Yan Zhang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Zhenwu Xiang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Lijia Ren
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
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4
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Misera L, Müller-Franzes G, Truhn D, Kather JN. Weakly Supervised Deep Learning in Radiology. Radiology 2024; 312:e232085. [PMID: 39041937 DOI: 10.1148/radiol.232085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard labels, typically applied manually by experts. In contrast, weakly supervised learning is more scalable. Weak supervision comprises situations in which only a portion of the data are labeled (incomplete supervision), labels refer to a whole region or case as opposed to a precisely delineated image region (inexact supervision), or labels contain errors (inaccurate supervision). In many applications, weak labels are sufficient to train useful models. Thus, weakly supervised learning can unlock a large amount of otherwise unusable data for training DL models. One example of this is using large language models to automatically extract weak labels from free-text radiology reports. Here, we outline the key concepts in weakly supervised learning and provide an overview of applications in radiologic image analysis. With more fundamental and clinical translational work, weakly supervised learning could facilitate the uptake of DL in radiology and research workflows by enabling large-scale image analysis and advancing the development of new DL-based biomarkers.
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Affiliation(s)
- Leo Misera
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Gustav Müller-Franzes
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Daniel Truhn
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
| | - Jakob Nikolas Kather
- From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.)
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Meng Y, Zhang Y, Xie J, Duan J, Joddrell M, Madhusudhan S, Peto T, Zhao Y, Zheng Y. Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment. Med Image Anal 2024; 95:103183. [PMID: 38692098 DOI: 10.1016/j.media.2024.103183] [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: 08/04/2023] [Revised: 01/26/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model's superior performance on segmentation and clinical function assessment.
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Affiliation(s)
- Yanda Meng
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Yuchen Zhang
- Center for Bioinformatics, Peking University, Beijing, China
| | - Jianyang Xie
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Martha Joddrell
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Savita Madhusudhan
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
| | - Tunde Peto
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Yitian Zhao
- Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, China; Ningbo Eye Hospital, Ningbo, China.
| | - Yalin Zheng
- Department of Eye and Vision Sciences, University of Liverpool, Liverpool, United Kingdom; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
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6
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Li Z, Zheng Y, Shan D, Yang S, Li Q, Wang B, Zhang Y, Hong Q, Shen D. ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2254-2265. [PMID: 38324425 DOI: 10.1109/tmi.2024.3363190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.
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7
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Yu K, Sun L, Chen J, Reynolds M, Chaudhary T, Batmanghelich K. DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images. Med Image Anal 2024; 92:103062. [PMID: 38086236 PMCID: PMC10872608 DOI: 10.1016/j.media.2023.103062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 08/24/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024]
Abstract
Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D lung CT images to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale datasets of lung CT scans show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework. Our codes are available at https://github.com/batmanlab/DrasCLR.
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Affiliation(s)
- Ke Yu
- School of Computing and Information, University of Pittsburgh, Pittsburgh, USA.
| | - Li Sun
- Department of Electrical and Computer Engineering, Boston University, Boston, USA
| | - Junxiang Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Maxwell Reynolds
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Tigmanshu Chaudhary
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Kayhan Batmanghelich
- Department of Electrical and Computer Engineering, Boston University, Boston, USA
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Liu X, Pan J, Zhang Y, Li X, Tang J. Semi-supervised contrast learning-based segmentation of choroidal vessel in optical coherence tomography images. Phys Med Biol 2023; 68:245005. [PMID: 37972415 DOI: 10.1088/1361-6560/ad0d42] [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: 08/14/2023] [Accepted: 11/16/2023] [Indexed: 11/19/2023]
Abstract
Objective.Choroidal vessels account for 85% of all blood vessels in the eye, and the accurate segmentation of choroidal vessels from optical coherence tomography (OCT) images provides important support for the quantitative analysis of choroid-related diseases and the development of treatment plans. Although deep learning-based methods have great potential for segmentation, these methods rely on large amounts of well-labeled data, and the data collection process is both time-consuming and laborious.Approach.In this paper, we propose a novel asymmetric semi-supervised segmentation framework called SSCR, based on a student-teacher model, to segment choroidal vessels in OCT images. The proposed framework enhances the segmentation results with uncertainty-aware self-integration and transformation consistency techniques. Meanwhile, we designed an asymmetric encoder-decoder network called Pyramid Pooling SegFormer (APP-SFR) for choroidal vascular segmentation. The network combines local attention and global attention information to improve the model's ability to learn complex vascular features. Additionally, we proposed a boundary repair module that enhances boundary confidence by utilizing a repair head to re-predict selected fuzzy points and further refines the segmentation boundary.Main results.We conducted extensive experiments on three different datasets: the ChorVessel dataset with 400 OCT images, the Meibomian Glands (MG) dataset with 400 images, and the U2OS Cell Nucleus Dataset with 200 images. The proposed method achieved an average Dice score of 74.23% on the ChorVessel dataset, which is 2.95% higher than the fully supervised network (U-Net) and outperformed other comparison methods. In both the MG dataset and the U2OS cell nucleus dataset, our proposed SSCR method achieved average Dice scores of 80.10% and 87.26%, respectively.Significance.The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art methods. The method is designed to help clinicians make rapid diagnoses of ophthalmic diseases and has potential for clinical application.
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Affiliation(s)
- Xiaoming Liu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China
| | - Jingling Pan
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China
| | - Ying Zhang
- Wuhan Aier Eye Hospital of Wuhan University, Wuhan, People's Republic of China
| | - Xiao Li
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China
| | - Jinshan Tang
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA 22030, United States of America
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Ying J, Huang W, Fu L, Yang H, Cheng J. Weakly supervised segmentation of uterus by scribble labeling on endometrial cancer MR images. Comput Biol Med 2023; 167:107582. [PMID: 37922606 DOI: 10.1016/j.compbiomed.2023.107582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/28/2023] [Accepted: 10/15/2023] [Indexed: 11/07/2023]
Abstract
Uterine segmentation of endometrial cancer MR images can be a valuable diagnostic tool for gynecologists. However, uterine segmentation based on deep learning relies on artificial pixel-level annotation, which is time-consuming, laborious and subjective. To reduce the dependence on pixel-level annotation, a method of weakly supervised uterine segmentation on endometrial cancer MRI slices is proposed, which only requires scribble label and is enhanced by pseudo-label technology, exponential geodesic distance loss and input disturbance strategy. Specifically, the limitations caused by the shortage of supervision are addressed by dynamically mixing the two outputs of the dual branch network to generate pseudo-labels, expanding supervision information and promoting mutual supervision training. On the other hand, considering the large difference of grayscale intensity between the uterus and surrounding tissues, the exponential geodesic distance loss is introduced to enhance the ability of the network to capture the edge of the uterus. Input disturbance strategies are incorporated to adapt to the flexible and variable characteristics of the uterus and further improve the segmentation performance of the network. The proposed method is evaluated on MRI images from 135 cases of endometrial cancer. Compared with other four weakly supervised segmentation methods, the performance of the proposed method is the best, whose mean DI, HD95, Recall, Precision, ADP are 92.8%, 11.632, 92.7%, 93.6%, 6.5% and increasing by 2.1%, 9.144, 0.6%, 2.4%, 2.9% respectively. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.
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Affiliation(s)
- Jie Ying
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Wei Huang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Haima Yang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jiangzihao Cheng
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
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10
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Lin L, Peng L, He H, Cheng P, Wu J, Wong KKY, Tang X. YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation. Med Image Anal 2023; 90:102937. [PMID: 37672901 DOI: 10.1016/j.media.2023.102937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 06/30/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.
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Affiliation(s)
- Li Lin
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Linkai Peng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Huaqing He
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Pujin Cheng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Jiewei Wu
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
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11
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Zhang J, Yang Y, Fang M, Xu Y, Ji Y, Chen M. A research on the improved rotational robustness for thoracic organ delineation by using joint learning of segmenting spatially-correlated organs: A U-net based comparison. J Appl Clin Med Phys 2023; 24:e14096. [PMID: 37469242 PMCID: PMC10647980 DOI: 10.1002/acm2.14096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 06/29/2023] [Indexed: 07/21/2023] Open
Abstract
PURPOSE To study the improved rotational robustness by using joint learning of spatially-correlated organ segmentation (SCOS) for thoracic organ delineation. The network structure is not our point. METHODS The SCOS was implemented in a U-net-like model (abbr. SCOS-net) and evaluated on unseen rotated test sets. Two hundred sixty-seven patients with thoracic tumors (232 without rotation and 35 with rotation) were enrolled. The training and validation images came from 61 randomly chosen unrotated patients. The test data included two sets. One consisted of 3000 slices from the rest 171 unrotated patients. They were rotated by us by -30°∼30°. One was the images from the 35 rotated patients. The lung, heart, and spinal cord were delineated by experienced radiation oncologists and regarded as ground truth. The SCOS-net was compared with its single-task learning counterparts, two published multiple learning task settings, and rotation augmentation. Dice, 3 distance metrics (maximum and 95th percentile of Hausdorff distances and average surface distance (ASD)) and the number of cases where ASD = infinity were adopted. We analyzed the results using visualization techniques. RESULTS In terms of no augmentation, the SCOS-net achieves the best lung and spinal cord segmentations and comparable heart delineation. With augmentation, SCOS performs better in some cases. CONCLUSION The proposed SCOS can improve rotational robustness, and is promising in clinical applications for its low network capacity and computational cost.
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Affiliation(s)
- Jie Zhang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
| | - Yiwei Yang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
| | - Min Fang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
| | - Yujin Xu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
| | - Yongling Ji
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
| | - Ming Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)HangzhouZhejiangChina
- Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhouZhejiangChina
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Lyu Y, Bennamoun M, Sharif N, Lip GYH, Dwivedi G. Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation. Life (Basel) 2023; 13:1870. [PMID: 37763273 PMCID: PMC10532509 DOI: 10.3390/life13091870] [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: 08/03/2023] [Revised: 08/19/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.
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Affiliation(s)
- Yiheng Lyu
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Naeha Sharif
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UK
- Liverpool John Moores University, Liverpool L3 5UX, UK
- Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Girish Dwivedi
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia
- Medical School, The University of Western Australia, Perth, WA 6009, Australia
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Du P, Niu X, Li X, Ying C, Zhou Y, He C, Lv S, Liu X, Du W, Wu W. Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging. BMC Bioinformatics 2023; 24:332. [PMID: 37667214 PMCID: PMC10478337 DOI: 10.1186/s12859-023-05435-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/02/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model. RESULTS The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively. CONCLUSION The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.
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Affiliation(s)
- Peng Du
- Hangzhou AiSmartIoT Co., Ltd., Hangzhou, Zhejiang, China
| | - Xiaofeng Niu
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Xukun Li
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chiqing Ying
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Yukun Zhou
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chang He
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Shuangzhi Lv
- Department of Radiology The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoli Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Weibo Du
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
| | - Wei Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
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Chang YH, Lin MY, Hsieh MT, Ou MC, Huang CR, Sheu BS. Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:394-404. [PMID: 37465459 PMCID: PMC10351611 DOI: 10.1109/jtehm.2023.3286423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/14/2023] [Accepted: 06/08/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVE Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain. METHODS AND PROCEDURES We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed. RESULTS Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results. CONCLUSION We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at https://github.com/nchucvml/MFADNet after acceptance. CLINICAL IMPACT Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.
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Affiliation(s)
- Ya-Han Chang
- Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichung402202Taiwan
| | - Meng-Ying Lin
- Department of Internal MedicineNational Cheng Kung University Hospital, College of Medicine, National Cheng Kung UniversityTainan701401Taiwan
| | - Ming-Tsung Hsieh
- Department of Internal MedicineNational Cheng Kung University Hospital, College of Medicine, National Cheng Kung UniversityTainan701401Taiwan
| | - Ming-Ching Ou
- Department of Medical ImageNational Cheng Kung University Hospital, College of Medicine, National Cheng Kung UniversityTainan701401Taiwan
| | - Chun-Rong Huang
- Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichung402202Taiwan
- Cross College Elite Program, and Academy of Innovative Semiconductor and Sustainable ManufacturingNational Cheng Kung UniversityTainan701401Taiwan
| | - Bor-Shyang Sheu
- Department of Internal MedicineNational Cheng Kung University Hospital, College of Medicine, National Cheng Kung UniversityTainan701401Taiwan
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15
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Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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16
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Wang G, Luo X, Gu R, Yang S, Qu Y, Zhai S, Zhao Q, Li K, Zhang S. PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107398. [PMID: 36773591 DOI: 10.1016/j.cmpb.2023.107398] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/29/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations. METHODS Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. RESULTS We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmentation with only 10% training images annotated; (3) Weakly supervised segmentation using scribble annotations; and (4) Learning from noisy labels for chest radiograph segmentation. CONCLUSIONS The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://github.com/HiLab-git/PyMIC.
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Affiliation(s)
- Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
| | - Xiangde Luo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Ran Gu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuojue Yang
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, USA
| | - Yijie Qu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuwei Zhai
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Qianfei Zhao
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Shaoting Zhang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
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Rodriguez-Obregon DE, Mejia-Rodriguez AR, Cendejas-Zaragoza L, Gutiérrez Mejía J, Arce-Santana ER, Charleston-Villalobos S, Aljama-Corrales T, Gabutti A, Santos-Díaz A. Semi-Supervised COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using Probabilistic Active Contour and CNN Segmentation. Biomed Signal Process Control 2023; 85:104905. [PMID: 36993838 PMCID: PMC10030333 DOI: 10.1016/j.bspc.2023.104905] [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: 08/04/2022] [Revised: 03/11/2023] [Accepted: 03/18/2023] [Indexed: 03/24/2023]
Abstract
Purpose A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks. Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1×10−4 in low-resolution and 5.1×10−5 for high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10% on average. Conclusion The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered as an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust and It may provide valuable information to differentiate between survived and deceased patients.
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Affiliation(s)
| | | | - Leopoldo Cendejas-Zaragoza
- Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Juan Gutiérrez Mejía
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Mexico City, Mexico
| | | | | | | | - Alejandro Gabutti
- Department of Radiology and Imaging, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Alejandro Santos-Díaz
- Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Mexico
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Lyu F, Ye M, Carlsen JF, Erleben K, Darkner S, Yuen PC. Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:797-809. [PMID: 36288236 DOI: 10.1109/tmi.2022.3217501] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.
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Meng Y, Zhang H, Zhao Y, Gao D, Hamill B, Patri G, Peto T, Madhusudhan S, Zheng Y. Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation With Dual Adaptive Graph Convolutional Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:416-429. [PMID: 36044486 DOI: 10.1109/tmi.2022.3203318] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Glaucoma is a progressive eye disease that results in permanent vision loss, and the vertical cup to disc ratio (vCDR) in colour fundus images is essential in glaucoma screening and assessment. Previous fully supervised convolution neural networks segment the optic disc (OD) and optic cup (OC) from color fundus images and then calculate the vCDR offline. However, they rely on a large set of labeled masks for training, which is expensive and time-consuming to acquire. To address this, we propose a weakly and semi-supervised graph-based network that investigates geometric associations and domain knowledge between segmentation probability maps (PM), modified signed distance function representations (mSDF), and boundary region of interest characteristics (B-ROI) in three aspects. Firstly, we propose a novel Dual Adaptive Graph Convolutional Network (DAGCN) to reason the long-range features of the PM and the mSDF w.r.t. the regional uniformity. Secondly, we propose a dual consistency regularization-based semi-supervised learning paradigm. The regional consistency between the PM and the mSDF, and the marginal consistency between the derived B-ROI from each of them boost the proposed model's performance due to the inherent geometric associations. Thirdly, we exploit the task-specific domain knowledge via the oval shapes of OD & OC, where a differentiable vCDR estimating layer is proposed. Furthermore, without additional annotations, the supervision on vCDR serves as weakly-supervisions for segmentation tasks. Experiments on six large-scale datasets demonstrate our model's superior performance on OD & OC segmentation and vCDR estimation. The implementation code has been made available.https://github.com/smallmax00/Dual_Adaptive_Graph_Reasoning.
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Zhuang M, Chen Z, Wang H, Tang H, He J, Qin B, Yang Y, Jin X, Yu M, Jin B, Li T, Kettunen L. Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images. Int J Comput Assist Radiol Surg 2023; 18:379-394. [PMID: 36048319 PMCID: PMC9889459 DOI: 10.1007/s11548-022-02730-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/29/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. METHODS We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. RESULTS For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. CONCLUSION Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.
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Affiliation(s)
- Mingrui Zhuang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Zhonghua Chen
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Hongkai Wang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
- Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China.
| | - Hong Tang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jiang He
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Bobo Qin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yuxin Yang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Xiaoxian Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Mengzhu Yu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Baitao Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Taijing Li
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Lauri Kettunen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
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Weakly-supervised localization and classification of biomarkers in OCT images with integrated reconstruction and attention. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sun W, Feng X, Liu J, Ma H. Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images. Biomed Signal Process Control 2023; 79:104099. [PMID: 35996574 PMCID: PMC9385774 DOI: 10.1016/j.bspc.2022.104099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/30/2022] [Accepted: 08/15/2022] [Indexed: 12/09/2022]
Abstract
At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.
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Jiang X, Xiao J, Zhang Q, Wang L, Jiang J, Lan K. Improved U-Net based on cross-layer connection for pituitary adenoma MRI image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:34-51. [PMID: 36650756 DOI: 10.3934/mbe.2023003] [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: 06/17/2023]
Abstract
Pituitary adenoma is a common neuroendocrine neoplasm, and most of its MR images are characterized by blurred edges, high noise and similar to surrounding normal tissues. Therefore, it is extremely difficult to accurately locate and outline the lesion of pituitary adenoma. To sovle these limitations, we design a novel deep learning framework for pituitary adenoma MRI image segmentation. Under the framework of U-Net, a newly cross-layer connection is introduced to capture richer multi-scale features and contextual information. At the same time, full-scale skip structure can reasonably utilize the above information obtained by different layers. In addition, an improved inception-dense block is designed to replace the classical convolution layer, which can enlarge the effectiveness of the receiving field and increase the depth of our network. Finally, a novel loss function based on binary cross-entropy and Jaccard losses is utilized to eliminate the problem of small samples and unbalanced data. The sample data were collected from 30 patients in Quzhou People's Hospital, with a total of 500 lesion images. Experimental results show that although the amount of patient sample is small, the proposed method has better performance in pituitary adenoma image compared with existing algorithms, and its Dice, Intersection over Union (IoU), Matthews correlation coefficient (Mcc) and precision reach 88.87, 80.67, 88.91 and 97.63%, respectively.
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Affiliation(s)
- Xiaoliang Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Junjian Xiao
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Qile Zhang
- Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
| | - Lihui Wang
- Department of Science and Education, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
| | - Jinyun Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Kun Lan
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
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Chen H, Jiang Y, Ko H, Loew M. A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images. Biomed Signal Process Control 2023; 79:104250. [PMID: 36188130 PMCID: PMC9510070 DOI: 10.1016/j.bspc.2022.104250] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/11/2022] [Accepted: 09/18/2022] [Indexed: 11/23/2022]
Abstract
Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase, caused by the difference in the image intensity and object region distribution between the training set and test set. In this paper, we proposed a novel unsupervised method for COVID-19 infection segmentation that aims to learn the domain-invariant features from lung cancer and COVID-19 images to improve the generalization ability of the segmentation network for use with COVID-19 CT images. First, to address the intensity difference, we proposed a novel data augmentation module based on Fourier Transform, which transfers the annotated lung cancer data into the style of COVID-19 image. Secondly, to reduce the distribution difference, we designed a teacher-student network to learn rotation-invariant features for segmentation. The experiments demonstrated that even without getting access to the annotations of the COVID-19 CT images during the training phase, the proposed network can achieve a state-of-the-art segmentation performance on COVID-19 infection.
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Affiliation(s)
- Han Chen
- School of Electrical Engineering, Korea University, Seoul, South Korea
| | - Yifan Jiang
- School of Electrical Engineering, Korea University, Seoul, South Korea
| | - Hanseok Ko
- School of Electrical Engineering, Korea University, Seoul, South Korea
| | - Murray Loew
- Biomedical Engineering, George Washington University, Washington D.C., USA
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Liu X, Zhou K, Yao J, Wang M, Zhang Y. Contrastive uncertainty based biomarkers detection in retinal optical coherence tomography images. Phys Med Biol 2022; 67. [PMID: 36384040 DOI: 10.1088/1361-6560/aca376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022]
Abstract
Objective.Retinal biomarker in optical coherence tomography (OCT) images plays a key guiding role in the follow-up diagnosis and clinical treatment of eye diseases. Although there have been many deep learning methods to automatically process retinal biomarker, the detection of retinal biomarkers is still a great challenge due to the similar characteristics to normal tissue, large changes in size and shape and fuzzy boundary of different types of biomarkers. To overcome these challenges, a novel contrastive uncertainty network (CUNet) is proposed for retinal biomarkers detection in OCT images.Approach.In CUNet, proposal contrastive learning is designed to enhance the feature representation of retinal biomarkers, aiming at boosting the discrimination ability of network between different types of retinal biomarkers. Furthermore, we proposed bounding box uncertainty and combined it with the traditional bounding box regression, thereby improving the sensitivity of the network to the fuzzy boundaries of retinal biomarkers, and to obtain a better localization result.Main results.Comprehensive experiments are conducted to evaluate the performance of the proposed CUNet. The experimental results on two datasets show that our proposed method achieves good detection performance compared with other detection methods.Significance.We propose a method for retinal biomarker detection trained by bounding box labels. The proposal contrastive learning and bounding box uncertainty are used to improve the detection of retinal biomarkers. The method is designed to help reduce the amount of work doctors have to do to detect retinal diseases.
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Affiliation(s)
- Xiaoming Liu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China.,Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China
| | - Kejie Zhou
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, People's Republic of China.,Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, People's Republic of China
| | - Junping Yao
- Department of Ophthalmology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, People's Republic of China
| | - Man Wang
- Wuhan Aier Eye Hospital of Wuhan University, Wuhan, People's Republic of China
| | - Ying Zhang
- Wuhan Aier Eye Hospital of Wuhan University, Wuhan, People's Republic of China
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Meng X, Fan J, Yu H, Mu J, Li Z, Yang A, Liu B, Lv K, Ai D, Lin Y, Song H, Fu T, Xiao D, Ma G, Yang J, Gu Y. Volume-awareness and outlier-suppression co-training for weakly-supervised MRI breast mass segmentation with partial annotations. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Fan C, Zeng Z, Xiao L, Qu X. GFNet: Automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features. PATTERN RECOGNITION 2022; 132:108963. [PMID: 35966970 PMCID: PMC9359771 DOI: 10.1016/j.patcog.2022.108963] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 07/31/2022] [Accepted: 08/07/2022] [Indexed: 05/03/2023]
Abstract
In early 2020, the global spread of the COVID-19 has presented the world with a serious health crisis. Due to the large number of infected patients, automatic segmentation of lung infections using computed tomography (CT) images has great potential to enhance traditional medical strategies. However, the segmentation of infected regions in CT slices still faces many challenges. Specially, the most core problem is the high variability of infection characteristics and the low contrast between the infected and the normal regions. This problem leads to fuzzy regions in lung CT segmentation. To address this problem, we have designed a novel global feature network(GFNet) for COVID-19 lung infections: VGG16 as backbone, we design a Edge-guidance module(Eg) that fuses the features of each layer. First, features are extracted by reverse attention module and Eg is combined with it. This series of steps enables each layer to fully extract boundary details that are difficult to be noticed by previous models, thus solving the fuzzy problem of infected regions. The multi-layer output features are fused into the final output to finally achieve automatic and accurate segmentation of infected areas. We compared the traditional medical segmentation networks, UNet, UNet++, the latest model Inf-Net, and methods of few shot learning field. Experiments show that our model is superior to the above models in Dice, Sensitivity, Specificity and other evaluation metrics, and our segmentation results are clear and accurate from the visual effect, which proves the effectiveness of GFNet. In addition, we verify the generalization ability of GFNet on another "never seen" dataset, and the results prove that our model still has better generalization ability than the above model. Our code has been shared at https://github.com/zengzhenhuan/GFNet.
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Affiliation(s)
- Chaodong Fan
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
- School of Computer Science, Xiangtan University, Xiangtan 411100, China
- Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University, Foshan 528000, China
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
| | - Zhenhuan Zeng
- School of Computer Science, Xiangtan University, Xiangtan 411100, China
| | - Leyi Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
- AnHui Key Laboratory of Detection Technology and Energy Saving Devices, AnHui Polytechnic University, Wuhu 241000, China
- Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou Normal University, Quanzhou 362000 China
- Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China
| | - Xilong Qu
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
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Kumar S, Mallik A. COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach. Neural Process Lett 2022; 55:1-24. [PMID: 36339644 PMCID: PMC9616430 DOI: 10.1007/s11063-022-11060-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2022] [Indexed: 10/31/2022]
Abstract
The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread of the disease. In this paper, we propose a novel Trained Output-based Transfer Learning (TOTL) approach for COVID-19 detection from chest X-rays. We start by preprocessing the Chest X-rays of the patients with techniques like denoising, contrasting, segmentation. These processed images are then fed to several pre-trained transfer learning models like InceptionV3, InceptionResNetV2, Xception, MobileNet, ResNet50, ResNet50V2, VGG16, and VGG19. We fine-tune these models on the processed chest X-rays. Then we further train the outputs of these models using a deep neural network architecture to achieve enhanced performance and aggregate the capabilities of each of them. The proposed model has been tested on four recent COVID-19 chest X-rays datasets by computing several popular evaluation metrics. The performance of our model has also been compared with various deep transfer learning models and several contemporary COVID-19 detection methods. The obtained results demonstrate the efficiency and efficacy of our proposed model.
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Affiliation(s)
- Sanjay Kumar
- Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042 India
| | - Abhishek Mallik
- Department of Computer Science and Engineering, Delhi Technological University, New Delhi, 110042 India
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29
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Connell M, Xin Y, Gerard SE, Herrmann J, Shah PK, Martin KT, Rezoagli E, Ippolito D, Rajaei J, Baron R, Delvecchio P, Humayun S, Rizi RR, Bellani G, Cereda M. Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN. Methods 2022; 205:200-209. [PMID: 35817338 PMCID: PMC9288584 DOI: 10.1016/j.ymeth.2022.07.007] [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: 11/05/2021] [Revised: 05/18/2022] [Accepted: 07/06/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data. METHODS We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists. RESULTS The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations. CONCLUSION Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.
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Affiliation(s)
- Marc Connell
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi Xin
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah E Gerard
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jacob Herrmann
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Parth K Shah
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin T Martin
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emanuele Rezoagli
- Department of Emergency and Intensive Care, San Gerardo Hospital, Monza, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Davide Ippolito
- Department of Diagnostic and Interventional Radiology, San Gerardo Hospital, Monza, Italy
| | - Jennia Rajaei
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ryan Baron
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paolo Delvecchio
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Shiraz Humayun
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA
| | - Rahim R Rizi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Giacomo Bellani
- Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maurizio Cereda
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
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Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning. Comput Biol Med 2022; 149:106033. [PMID: 36041270 PMCID: PMC9419627 DOI: 10.1016/j.compbiomed.2022.106033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/23/2022] [Accepted: 08/20/2022] [Indexed: 11/20/2022]
Abstract
Medical image segmentation is a key initial step in several therapeutic applications. While most of the automatic segmentation models are supervised, which require a well-annotated paired dataset, we introduce a novel annotation-free pipeline to perform segmentation of COVID-19 CT images. Our pipeline consists of three main subtasks: automatically generating a 3D pseudo-mask in self-supervised mode using a generative adversarial network (GAN), leveraging the quality of the pseudo-mask, and building a multi-objective segmentation model to predict lesions. Our proposed 3D GAN architecture removes infected regions from COVID-19 images and generates synthesized healthy images while keeping the 3D structure of the lung the same. Then, a 3D pseudo-mask is generated by subtracting the synthesized healthy images from the original COVID-19 CT images. We enhanced pseudo-masks using a contrastive learning approach to build a region-aware segmentation model to focus more on the infected area. The final segmentation model can be used to predict lesions in COVID-19 CT images without any manual annotation at the pixel level. We show that our approach outperforms the existing state-of-the-art unsupervised and weakly-supervised segmentation techniques on three datasets by a reasonable margin. Specifically, our method improves the segmentation results for the CT images with low infection by increasing sensitivity by 20% and the dice score up to 4%. The proposed pipeline overcomes some of the major limitations of existing unsupervised segmentation approaches and opens up a novel horizon for different applications of medical image segmentation.
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31
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Jiang S, Li J, Hua Z. Transformer with progressive sampling for medical cellular image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12104-12126. [PMID: 36653988 DOI: 10.3934/mbe.2022563] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The convolutional neural network, as the backbone network for medical image segmentation, has shown good performance in the past years. However, its drawbacks cannot be ignored, namely, convolutional neural networks focus on local regions and are difficult to model global contextual information. For this reason, transformer, which is used for text processing, was introduced into the field of medical segmentation, and thanks to its expertise in modelling global relationships, the accuracy of medical segmentation was further improved. However, the transformer-based network structure requires a certain training set size to achieve satisfactory segmentation results, and most medical segmentation datasets are small in size. Therefore, in this paper we introduce a gated position-sensitive axial attention mechanism in the self-attention module, so that the transformer-based network structure can also be adapted to the case of small datasets. The common operation of the visual transformer introduced to visual processing when dealing with segmentation tasks is to divide the input image into equal patches of the same size and then perform visual processing on each patch, but this simple division may lead to the destruction of the structure of the original image, and there may be large unimportant regions in the divided grid, causing attention to stay on the uninteresting regions, affecting the segmentation performance. Therefore, in this paper, we add iterative sampling to update the sampling positions, so that the attention stays on the region to be segmented, reducing the interference of irrelevant regions and further improving the segmentation performance. In addition, we introduce the strip convolution module (SCM) and pyramid pooling module (PPM) to capture the global contextual information. The proposed network is evaluated on several datasets and shows some improvement in segmentation accuracy compared to networks of recent years.
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Affiliation(s)
- Shen Jiang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China
| | - Jinjiang Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China
| | - Zhen Hua
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China
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Li H, Liu X, Jia D, Chen Y, Hou P, Li H. Research on chest radiography recognition model based on deep learning. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:11768-11781. [PMID: 36124613 DOI: 10.3934/mbe.2022548] [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: 06/15/2023]
Abstract
With the development of medical informatization and against the background of the spread of global epidemic, the demand for automated chest X-ray detection by medical personnel and patients continues to increase. Although the rapid development of deep learning technology has made it possible to automatically generate a single conclusive sentence, the results produced by existing methods are not reliable enough due to the complexity of medical images. To solve this problem, this paper proposes an improved RCLN (Recurrent Learning Network) model as a solution. The model can generate high-level conclusive impressions and detailed descriptive findings sentence-by-sentence and realize the imitation of the doctoros standard tone by combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network through a recurrent structure, and adding a multi-head attention mechanism. The proposed algorithm has been experimentally verified on publicly available chest X-ray images from the Open-i image set. The results show that it can effectively solve the problem of automatic generation of colloquial medical reports.
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Affiliation(s)
- Hui Li
- School of Computer Engineering, Jiangsu Ocean University, China
| | - Xintang Liu
- School of Computer Engineering, Jiangsu Ocean University, China
| | - Dongbao Jia
- School of Computer Engineering, Jiangsu Ocean University, China
| | - Yanyan Chen
- School of Computer Engineering, Jiangsu Ocean University, China
| | - Pengfei Hou
- School of Computer Engineering, Jiangsu Ocean University, China
| | - Haining Li
- Department of Neurology, General Hospital of Ningxia Medical University, China
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COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach. Diagnostics (Basel) 2022; 12:diagnostics12081805. [PMID: 35892518 PMCID: PMC9332359 DOI: 10.3390/diagnostics12081805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/14/2022] [Accepted: 07/23/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Automated segmentation of COVID-19 infection lesions and the assessment of the severity of the infections are critical in COVID-19 diagnosis and treatment. Based on a large amount of annotated data, deep learning approaches have been widely used in COVID-19 medical image analysis. However, the number of medical image samples is generally huge, and it is challenging to obtain enough annotated medical images for training a deep CNN model. Methods: To address these challenges, we propose a novel self-supervised deep learning method for automated segmentation of COVID-19 infection lesions and assessing the severity of infection, which can reduce the dependence on the annotation of the training samples. In the proposed method, first, many unlabeled data are used to pre-train an encoder-decoder model to learn rotation-dependent and rotation-invariant features. Then, a small amount of labeled data is used to fine-tune the pre-trained encoder-decoder for COVID-19 severity classification and lesion segmentation. Results: The proposed methods were tested on two public COVID-19 CT datasets and one self-built dataset. Accuracy, precision, recall, and F1-score were used to measure classification performance and Dice coefficient was used to measure segmentation performance. For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. Conclusions: The proposed method provides better classification and segmentation performance under limited labeled data than other methods.
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Sun J, Pi P, Tang C, Wang SH, Zhang YD. TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model. Comput Biol Med 2022; 146:105531. [PMID: 35489140 PMCID: PMC9013277 DOI: 10.1016/j.compbiomed.2022.105531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/12/2022] [Accepted: 04/13/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases. METHODS To address the shortcomings of commonly used pre-training methods and exploit the information in unlabeled images, we proposed a new pre-training method based on transfer learning with self-supervised learning (TS). After that, a new convolutional neural network based on attention mechanism and deep residual network (RANet) was proposed to extract features. Based on this, a hybrid ensemble model (TSRNet) was proposed for classifying lung CT images of suspected patients as COVID-19 and normal. RESULTS Compared with the existing five models in terms of accuracy (DarkCOVIDNet: 98.08%; Deep-COVID: 97.58%; NAGNN: 97.86%; COVID-ResNet: 97.78%; Patch-based CNN: 88.90%), TSRNet has the highest accuracy of 99.80%. In addition, the recall, f1-score, and AUC of the model reached 99.59%, 99.78%, and 1, respectively. CONCLUSION TSRNet can effectively diagnose suspected COVID-19 cases with the help of the information in unlabeled and labeled images, thus helping physicians to adopt early treatment plans for confirmed cases.
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Affiliation(s)
- Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Pengpeng Pi
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Chaosheng Tang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Shui-Hua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Yu-Dong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
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Li P, Wang X, Liu P, Xu T, Sun P, Dong B, Xue H. Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3241422. [PMID: 35607393 PMCID: PMC9124126 DOI: 10.1155/2022/3241422] [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: 01/31/2022] [Revised: 04/24/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
Abstract
Objective In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. Methods The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. Results The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. Conclusions It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients.
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Affiliation(s)
- Ping Li
- Department of Gynecology and Obstetrics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian, China
| | - Xiaoxia Wang
- School of Medicine, Huaqiao University, Quanzhou 362000, Fujian, China
| | - Peizhong Liu
- School of Medicine, Huaqiao University, Quanzhou 362000, Fujian, China
- College of Engineering, Huaqiao University, Quanzhou 362000, Fujian, China
| | - Tianxiang Xu
- College of Engineering, Huaqiao University, Quanzhou 362000, Fujian, China
| | - Pengming Sun
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou 350001, Fujian, China
| | - Binhua Dong
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou 350001, Fujian, China
| | - Huifeng Xue
- Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou 350001, Fujian, China
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Guo P, Li L, Li C, Huang W, Zhao G, Wang S, Wang M, Lin Y. Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7315665. [PMID: 35591941 PMCID: PMC9113909 DOI: 10.1155/2022/7315665] [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: 12/12/2021] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 11/17/2022]
Abstract
Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.
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Affiliation(s)
- Peiying Guo
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
| | - Longfei Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Weijian Huang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Guohua Zhao
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Meiyun Wang
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China
- Hanwei IoT Institute, Zhengzhou University, Zhengzhou 450002, China
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COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6216273. [PMID: 35422979 PMCID: PMC9002900 DOI: 10.1155/2022/6216273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 12/12/2022]
Abstract
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed "COV-DLS" consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3. An attempt is made to modify these neoteric architectures by incorporating the average pooling and dense layers. The dropout layer is also added to prevent the overfitting problem. Two dense layers with different activation functions are also added. Thereafter, the output of these modified models is applied during the classification phase, when COV-DLS are applied on a COVID-19 chest X-ray image data set. Classification accuracy of 98.61% is achieved by Modified-VGG16, 97.22% by Modified-VGG19, 95.13% by Modified-ResNet50, and 99.31% by Modified-InceptionV3. COV-DLS outperforms existing deep learning models in terms of accuracy and F1-score.
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Zhang X, Wang G, Zhao SG. CapsNet-COVID19: Lung CT image classification method based on CapsNet model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5055-5074. [PMID: 35430853 DOI: 10.3934/mbe.2022236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.
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Affiliation(s)
- XiaoQing Zhang
- Nanjing University of Science and Technology, Taizhou Technology Institute, Taizhou 225300, China
| | - GuangYu Wang
- Donghua University, College of Information Science and Technology, Shanghai 201620, China
| | - Shu-Guang Zhao
- Donghua University, College of Information Science and Technology, Shanghai 201620, China
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Suzuki Y, Kido S, Mabu S, Yanagawa M, Tomiyama N, Sato Y. Segmentation of Diffuse Lung Abnormality Patterns on Computed Tomography Images using Partially Supervised Learning. ADVANCED BIOMEDICAL ENGINEERING 2022. [DOI: 10.14326/abe.11.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Yuki Suzuki
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine
| | - Shoji Kido
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine
| | - Shingo Mabu
- Graduate School of Sciences and Technology for Innovation, Yamaguchi University
| | - Masahiro Yanagawa
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine
| | - Noriyuki Tomiyama
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine
| | - Yoshinobu Sato
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology
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Detection and Prevention of Virus Infection. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1368:21-52. [DOI: 10.1007/978-981-16-8969-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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