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Liu M, Wu S, Chen R, Lin Z, Wang Y, Meijering E. Brain Image Segmentation for Ultrascale Neuron Reconstruction via an Adaptive Dual-Task Learning Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2574-2586. [PMID: 38373129 DOI: 10.1109/tmi.2024.3367384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution of background noise, posing an enormous challenge to neuron reconstruction from whole brain images. In this paper, we propose an adaptive dual-task learning network (ADTL-Net) to quickly and accurately extract neuronal structures from ultrascale brain images. Specifically, this framework includes an External Features Classifier (EFC) and a Parameter Adaptive Segmentation Decoder (PASD), which share the same Multi-Scale Feature Encoder (MSFE). MSFE introduces an attention module named Channel Space Fusion Module (CSFM) to extract structure and intensity distribution features of neurons at different scales for addressing the problem of anisotropy in 3D space. Then, EFC is designed to classify these feature maps based on external features, such as foreground intensity distributions and image smoothness, and select specific PASD parameters to decode them of different classes to obtain accurate segmentation results. PASD contains multiple sets of parameters trained by different representative complex signal-to-noise distribution image blocks to handle various images more robustly. Experimental results prove that compared with other advanced segmentation methods for neuron reconstruction, the proposed method achieves state-of-the-art results in the task of neuron reconstruction from ultrascale brain images, with an improvement of about 49% in speed and 12% in F1 score.
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Zhang X, Tian L, Guo S, Liu Y. STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation. BIOMED ENG-BIOMED TE 2024; 0:bmt-2023-0121. [PMID: 38712825 DOI: 10.1515/bmt-2023-0121] [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: 01/15/2023] [Accepted: 04/15/2024] [Indexed: 05/08/2024]
Abstract
Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with a hybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.
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Affiliation(s)
- Xiufeng Zhang
- School of Mechanical and Electrical Engineering, 66455 Dalian Minzu University , Dalian, Liaoning, China
| | - Lingzhuo Tian
- School of Mechanical and Electrical Engineering, 66455 Dalian Minzu University , Dalian, Liaoning, China
| | - Shengjin Guo
- School of Mechanical and Electrical Engineering, 66455 Dalian Minzu University , Dalian, Liaoning, China
| | - Yansong Liu
- School of Mechanical and Electrical Engineering, 66455 Dalian Minzu University , Dalian, Liaoning, China
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Zeng Y, Wang Y. Complete Neuron Reconstruction Based on Branch Confidence. Brain Sci 2024; 14:396. [PMID: 38672045 PMCID: PMC11047972 DOI: 10.3390/brainsci14040396] [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: 03/04/2024] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.
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Affiliation(s)
- Ying Zeng
- School of Computer Science and Technology, Shanghai University, Shanghai 200444, China;
- Guangdong Institute of Intelligence Science and Technology, Zhuhai 519031, China
| | - Yimin Wang
- Guangdong Institute of Intelligence Science and Technology, Zhuhai 519031, China
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Toma TT, Wang Y, Gahlmann A, Acton ST. DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications. EXPERT SYSTEMS WITH APPLICATIONS 2024; 238:122094. [PMID: 38646063 PMCID: PMC11027476 DOI: 10.1016/j.eswa.2023.122094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Accurate and automatic segmentation of individual cell instances in microscopy images is a vital step for quantifying the cellular attributes, which can subsequently lead to new discoveries in biomedical research. In recent years, data-driven deep learning techniques have shown promising results in this task. Despite the success of these techniques, many fail to accurately segment cells in microscopy images with high cell density and low signal-to-noise ratio. In this paper, we propose a novel 3D cell segmentation approach DeepSeeded, a cascaded deep learning architecture that estimates seeds for a classical seeded watershed segmentation. The cascaded architecture enhances the cell interior and border information using Euclidean distance transforms and detects the cell seeds by performing voxel-wise classification. The data-driven seed estimation process proposed here allows segmenting touching cell instances from a dense, intensity-inhomogeneous microscopy image volume. We demonstrate the performance of the proposed method in segmenting 3D microscopy images of a particularly dense cell population called bacterial biofilms. Experimental results on synthetic and two real biofilm datasets suggest that the proposed method leads to superior segmentation results when compared to state-of-the-art deep learning methods and a classical method.
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Affiliation(s)
- Tanjin Taher Toma
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, 22904, Virginia, USA
| | - Yibo Wang
- Department of Chemistry, University of Virginia, Charlottesville, 22904, Virginia, USA
| | - Andreas Gahlmann
- Department of Chemistry, University of Virginia, Charlottesville, 22904, Virginia, USA
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, 22903, Virginia, USA
| | - Scott T. Acton
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, 22904, Virginia, USA
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Ren J, Che J, Gong P, Wang X, Li X, Li A, Xiao C. Cross comparison representation learning for semi-supervised segmentation of cellular nuclei in immunofluorescence staining. Comput Biol Med 2024; 171:108102. [PMID: 38350398 DOI: 10.1016/j.compbiomed.2024.108102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/29/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
The morphological analysis of cells from optical images is vital for interpreting brain function in disease states. Extracting comprehensive cell morphology from intricate backgrounds, common in neural and some medical images, poses a significant challenge. Due to the huge workload of manual recognition, automated neuron cell segmentation using deep learning algorithms with labeled data is integral to neural image analysis tools. To combat the high cost of acquiring labeled data, we propose a novel semi-supervised cell segmentation algorithm for immunofluorescence-stained cell image datasets (ISC), utilizing a mean-teacher semi-supervised learning framework. We include a "cross comparison representation learning block" to enhance the teacher-student model comparison on high-dimensional channels, thereby improving feature compactness and separability, which results in the extraction of higher-dimensional features from unlabeled data. We also suggest a new network, the Multi Pooling Layer Attention Dense Network (MPAD-Net), serving as the backbone of the student model to augment segmentation accuracy. Evaluations on the immunofluorescence staining datasets and the public CRAG dataset illustrate our method surpasses other top semi-supervised learning methods, achieving average Jaccard, Dice and Normalized Surface Dice (NSD) indicators of 83.22%, 90.95% and 81.90% with only 20% labeled data. The datasets and code are available on the website at https://github.com/Brainsmatics/CCRL.
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Affiliation(s)
- Jianran Ren
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Jingyi Che
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Peicong Gong
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Xiaojun Wang
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Xiangning Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Anan Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chi Xiao
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China.
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Chang GH, Wu MY, Yen LH, Huang DY, Lin YH, Luo YR, Liu YD, Xu B, Leong KW, Lai WS, Chiang AS, Wang KC, Lin CH, Wang SL, Chu LA. Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107991. [PMID: 38185040 DOI: 10.1016/j.cmpb.2023.107991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/10/2023] [Accepted: 12/19/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND AND OBJECTIVE Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.
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Affiliation(s)
- Gary Han Chang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC; Graduate School of Advanced Technology, National Taiwan University, Taipei, Taiwan, ROC.
| | - Meng-Yun Wu
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC
| | - Ling-Hui Yen
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Da-Yu Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ROC
| | - Ya-Hui Lin
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Yi-Ru Luo
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Ya-Ding Liu
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Bin Xu
- Department of Psychiatry, Columbia University, New York, NY 10032, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Wen-Sung Lai
- Department of Psychology, National Taiwan University, Taipei, Taiwan, ROC
| | - Ann-Shyn Chiang
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC; Institute of System Neuroscience, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Kuo-Chuan Wang
- Department of Neurosurgery, National Taiwan University Hospital, Taipei, Taiwan, ROC
| | - Chin-Hsien Lin
- Department of Neurosurgery, National Taiwan University Hospital, Taipei, Taiwan, ROC
| | - Shih-Luen Wang
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University, Boston, MA 02115, USA
| | - Li-An Chu
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC; Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan, ROC.
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Chen R, Liu M, Chen W, Wang Y, Meijering E. Deep learning in mesoscale brain image analysis: A review. Comput Biol Med 2023; 167:107617. [PMID: 37918261 DOI: 10.1016/j.compbiomed.2023.107617] [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: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.
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Affiliation(s)
- Runze Chen
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Min Liu
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China; Research Institute of Hunan University in Chongqing, Chongqing, 401135, China.
| | - Weixun Chen
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Yaonan Wang
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia
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Jiang X, Zhu Y, Liu Y, Wang N, Yi L. MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107846. [PMID: 37806121 DOI: 10.1016/j.cmpb.2023.107846] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Fusing the CNN and Transformer in the encoder has recently achieved outstanding performance in medical image segmentation. However, two obvious limitations require addressing: (1) The utilization of Transformer leads to heavy parameters, and its intricate structure demands ample data and resources for training, and (2) most previous research had predominantly focused on enhancing the performance of the feature encoder, with little emphasis placed on the design of the feature decoder. METHODS To this end, we propose a novel MLP-CNN based dual-path complementary (MC-DC) network for medical image segmentation, which replaces the complex Transformer with a cost-effective Multi-Layer Perceptron (MLP). Specifically, a dual-path complementary (DPC) module is designed to effectively fuse multi-level features from MLP and CNN. To respectively reconstruct global and local information, the dual-path decoder is proposed which is mainly composed of cross-scale global feature fusion (CS-GF) module and cross-scale local feature fusion (CS-LF) module. Moreover, we leverage a simple and efficient segmentation mask feature fusion (SMFF) module to merge the segmentation outcomes generated by the dual-path decoder. RESULTS Comprehensive experiments were performed on three typical medical image segmentation tasks. For skin lesions segmentation, our MC-DC network achieved 91.69% Dice and 9.52mm ASSD on the ISIC2018 dataset. In addition, the 91.6% Dice and 94.4% Dice were respectively obtained on the Kvasir-SEG dataset and CVC-ClinicDB dataset for polyp segmentation. Moreover, we also conducted experiments on the private COVID-DS36 dataset for lung lesion segmentation. Our MC-DC has achieved 87.6% [87.1%, 88.1%], and 92.3% [91.8%, 92.7%] on ground-glass opacity, interstitial infiltration, and lung consolidation, respectively. CONCLUSIONS The experimental results indicate that the proposed MC-DC network exhibits exceptional generalization capability and surpasses other state-of-the-art methods in higher results and lower computational complexity.
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Affiliation(s)
- Xiaoben Jiang
- School of Information Science and Technology, East China University of Science and Technology, Shanghai, 200237, China
| | - Yu Zhu
- School of Information Science and Technology, East China University of Science and Technology, Shanghai, 200237, China.
| | - Yatong Liu
- School of Information Science and Technology, East China University of Science and Technology, Shanghai, 200237, China
| | - Nan Wang
- School of Information Science and Technology, East China University of Science and Technology, Shanghai, 200237, China
| | - Lei Yi
- Department of Burn, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Fan M, Huang G, Lou J, Gao X, Zeng T, Li L. Cross-Parametric Generative Adversarial Network-Based Magnetic Resonance Image Feature Synthesis for Breast Lesion Classification. IEEE J Biomed Health Inform 2023; 27:5495-5505. [PMID: 37656652 DOI: 10.1109/jbhi.2023.3311021] [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: 09/03/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains information on tumor morphology and physiology for breast cancer diagnosis and treatment. However, this technology requires contrast agent injection with more acquisition time than other parametric images, such as T2-weighted imaging (T2WI). Current image synthesis methods attempt to map the image data from one domain to another, whereas it is challenging or even infeasible to map the images with one sequence into images with multiple sequences. Here, we propose a new approach of cross-parametric generative adversarial network (GAN)-based feature synthesis (CPGANFS) to generate discriminative DCE-MRI features from T2WI with applications in breast cancer diagnosis. The proposed approach decodes the T2W images into latent cross-parameter features to reconstruct the DCE-MRI and T2WI features by balancing the information shared between the two. A Wasserstein GAN with a gradient penalty is employed to differentiate the T2WI-generated features from ground-truth features extracted from DCE-MRI. The synthesized DCE-MRI feature-based model achieved significantly (p = 0.036) higher prediction performance (AUC = 0.866) in breast cancer diagnosis than that based on T2WI (AUC = 0.815). Visualization of the model shows that our CPGANFS method enhances the predictive power by levitating attention to the lesion and the surrounding parenchyma areas, which is driven by the interparametric information learned from T2WI and DCE-MRI. Our proposed CPGANFS provides a framework for cross-parametric MR image feature generation from a single-sequence image guided by an information-rich, time-series image with kinetic information. Extensive experimental results demonstrate its effectiveness with high interpretability and improved performance in breast cancer diagnosis.
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Liu Y, Wang G, Ascoli GA, Zhou J, Liu L. Neuron tracing from light microscopy images: automation, deep learning and bench testing. Bioinformatics 2022; 38:5329-5339. [PMID: 36303315 PMCID: PMC9750132 DOI: 10.1093/bioinformatics/btac712] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications. RESULTS This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.
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Affiliation(s)
- Yufeng Liu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Gaoyu Wang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Jiangning Zhou
- Institute of Brain Science, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Lijuan Liu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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