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Bacon EJ, He D, Achi NAD, Wang L, Li H, Yao-Digba PDZ, Monkam P, Qi S. Neuroimage analysis using artificial intelligence approaches: a systematic review. Med Biol Eng Comput 2024; 62:2599-2627. [PMID: 38664348 DOI: 10.1007/s11517-024-03097-w] [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: 10/10/2023] [Accepted: 04/14/2024] [Indexed: 08/18/2024]
Abstract
In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.
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Affiliation(s)
- Eric Jacob Bacon
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | | | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Han Li
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
| | | | - Patrice Monkam
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
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2
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Ma F, Wang S, Guo Y, Dai C, Meng J. Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification. BIOMED ENG-BIOMED TE 2024; 69:307-315. [PMID: 38178615 DOI: 10.1515/bmt-2023-0266] [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: 06/16/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVES Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. METHODS We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. RESULTS In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels. CONCLUSIONS The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.
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Affiliation(s)
- Fei Ma
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Shengbo Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Yanfei Guo
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Cuixia Dai
- Department of College Science, Shanghai Institute of Technology, Shanghai, Shanghai, China
| | - Jing Meng
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
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Priori EC, Ratto D, De Luca F, Sandionigi A, Savino E, Giammello F, Romeo M, Brandalise F, Roda E, Rossi P. Hericium erinaceus Extract Exerts Beneficial Effects on Gut-Neuroinflammaging-Cognitive Axis in Elderly Mice. BIOLOGY 2023; 13:18. [PMID: 38248449 PMCID: PMC10813749 DOI: 10.3390/biology13010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/23/2024]
Abstract
Ageing is a biological phenomenon that determines the impairment of cognitive performances, in particular, affecting memory. Inflammation and cellular senescence are known to be involved in the pathogenesis of cognitive decline. The gut microbiota-brain axis could exert a critical role in influencing brain homeostasis during ageing, modulating neuroinflammation, and possibly leading to inflammaging. Due to their anti-ageing properties, medicinal mushrooms can be utilised as a resource for developing pharmaceuticals and functional foods. Specifically, Hericium erinaceus (He), thanks to its bioactive metabolites, exerts numerous healthy beneficial effects, such as reinforcing the immune system, counteracting ageing, and improving cognitive performance. Our previous works demonstrated the capabilities of two months of He1 standardised extract oral supplementation in preventing cognitive decline in elderly frail mice. Herein, we showed that this treatment did not change the overall gut microbiome composition but significantly modified the relative abundance of genera specifically involved in cognition and inflammation. Parallelly, a significant decrease in crucial markers of inflammation and cellular senescence, i.e., CD45, GFAP, IL6, p62, and γH2AX, was demonstrated in the dentate gyrus and Cornus Ammonis hippocampal areas through immunohistochemical experiments. In summary, we suggested beneficial and anti-inflammatory properties of He1 in mouse hippocampus through the gut microbiome-brain axis modulation.
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Affiliation(s)
- Erica Cecilia Priori
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
| | - Daniela Ratto
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
| | - Fabrizio De Luca
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
| | - Anna Sandionigi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126 Milan, Italy;
- Quantia Consulting S.r.l., Via Petrarca 20, 22066 Mariano Comense, Italy
| | - Elena Savino
- Department of Earth and Environmental Science, University of Pavia, 27100 Pavia, Italy;
| | - Francesca Giammello
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
| | - Marcello Romeo
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
| | | | - Elisa Roda
- Laboratory of Clinical & Experimental Toxicology, Pavia Poison Centre, National Toxicology Information Centre, Toxicology Unit, Istituti Clinici Scientifici Maugeri IRCCS, 27100 Pavia, Italy
| | - Paola Rossi
- Department of Biology and Biotechnology “L. Spallanzani”, University of Pavia, 27100 Pavia, Italy; (E.C.P.); (D.R.); (F.D.L.); (F.G.); (M.R.)
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Yang Q, Cai S, Chen G, Yu X, Cattell RF, Raviv TR, Huang C, Zhang N, Gao Y. Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80. Front Neurosci 2023; 17:1162096. [PMID: 37719158 PMCID: PMC10501455 DOI: 10.3389/fnins.2023.1162096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/26/2023] [Indexed: 09/19/2023] Open
Abstract
The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Shuxiu Cai
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Guojing Chen
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaxia Yu
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Renee F. Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States
- Department of Radiation Oncology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Tammy Riklin Raviv
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Chuan Huang
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States
- Department of Radiology, Stony Brook University, Stony Brook, NY, United States
| | - Nu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
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Wang J, Huang S, Wang Z, Huang D, Qin J, Wang H, Wang W, Liang Y. A calibrated SVM based on weighted smooth GL1/2 for Alzheimer’s disease prediction. Comput Biol Med 2023; 158:106752. [PMID: 37003069 DOI: 10.1016/j.compbiomed.2023.106752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/17/2023] [Accepted: 03/06/2023] [Indexed: 03/31/2023]
Abstract
Alzheimer's disease (AD) is currently one of the mainstream senile diseases in the world. It is a key problem predicting the early stage of AD. Low accuracy recognition of AD and high redundancy brain lesions are the main obstacles. Traditionally, Group Lasso method can achieve good sparseness. But, redundancy inside group is ignored. This paper proposes an improved smooth classification framework which combines the weighted smooth GL1/2 (wSGL1/2) as feature selection method and a calibrated support vector machine (cSVM) as the classifier. wSGL1/2 can make intra-group and inner-group features sparse, in which the group weights can further improve the efficiency of the model. cSVM can enhance the speed and stability of model by adding calibrated hinge function. Before feature selecting, an anatomical boundary-based clustering, called as ac-SLIC-AAL, is designed to make adjacent similar voxels into one group for accommodating the overall differences of all data. The cSVM model is fast convergence speed, high accuracy and good interpretability on AD classification, AD early diagnosis and MCI transition prediction. In experiments, all steps are tested respectively, including classifiers' comparison, feature selection verification, generalization verification and comparing with state-of-the-art methods. The results are supportive and satisfactory. The superior of the proposed model are verified globally. At the same time, the algorithm can point out the important brain areas in the MRI, which has important reference value for the doctor's predictive work. The source code and data is available at http://github.com/Hu-s-h/c-SVMForMRI.
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Affiliation(s)
- Jinfeng Wang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China.
| | - Shuaihui Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Zhiwen Wang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Dong Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Jing Qin
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Hui Wang
- School of EEECS, Queen's University Belfast, Belfast, UK
| | - Wenzhong Wang
- College of Economics and Management, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Yong Liang
- Peng Cheng Laboratory, 518005, Shenzhen, Guangdong, China
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Sakashita K, Akiyama Y, Hirano T, Sasagawa A, Arihara M, Kuribara T, Ochi S, Enatsu R, Mikami T, Mikuni N. Deep learning for the diagnosis of mesial temporal lobe epilepsy. PLoS One 2023; 18:e0282082. [PMID: 36821567 PMCID: PMC9949622 DOI: 10.1371/journal.pone.0282082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
OBJECTIVE This study aimed to enable the automatic detection of the hippocampus and diagnose mesial temporal lobe epilepsy (MTLE) with the hippocampus as the epileptogenic area using artificial intelligence (AI). We compared the diagnostic accuracies of AI and neurosurgical physicians for MTLE with the hippocampus as the epileptogenic area. METHOD In this study, we used an AI program to diagnose MTLE. The image sets were processed using a code written in Python 3.7.4. and analyzed using Open Computer Vision 4.5.1. The deep learning model, which was a fine-tuned VGG16 model, consisted of several layers. The diagnostic accuracies of AI and board-certified neurosurgeons were compared. RESULTS AI detected the hippocampi automatically and diagnosed MTLE with the hippocampus as the epileptogenic area on both T2-weighted imaging (T2WI) and fluid-attenuated inversion recovery (FLAIR) images. The diagnostic accuracies of AI based on T2WI and FLAIR data were 99% and 89%, respectively, and those of neurosurgeons based on T2WI and FLAIR data were 94% and 95%, respectively. The diagnostic accuracy of AI was statistically higher than that of board-certified neurosurgeons based on T2WI data (p = 0.00129). CONCLUSION The deep learning-based AI program is highly accurate and can diagnose MTLE better than some board-certified neurosurgeons. AI can maintain a certain level of output accuracy and can be a reliable assistant to doctors.
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Affiliation(s)
- Kyoya Sakashita
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Yukinori Akiyama
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Tsukasa Hirano
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Ayaka Sasagawa
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Masayasu Arihara
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | | | - Satoko Ochi
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Rei Enatsu
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Takeshi Mikami
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
| | - Nobuhiro Mikuni
- Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan
- * E-mail:
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Cognitive Healthy Aging in Mice: Boosting Memory by an Ergothioneine-Rich Hericium erinaceus Primordium Extract. BIOLOGY 2023; 12:biology12020196. [PMID: 36829475 PMCID: PMC9953177 DOI: 10.3390/biology12020196] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Brain aging is a crucial risk factor for several neurodegenerative disorders and dementia. The most affected cognitive function is memory, worsening early during aging. Inflammation and oxidative stress are known to have a role in pathogenesis of cognitive impairments, and a link exists between aging/frailty and immunosenescence/inflammaging. Based on anti-aging properties, medicinal mushrooms represent a source to develop medicines and functional foods. In particular, Hericium erinaceus (He) displays several actions ranging from boosting the immune system to fighting senescence, due to its active ingredients/metabolites. Among these, Ergothioneine (ERGO) is known as the longevity vitamin. Currently, we demonstrated the efficacy of an ERGO-rich He primordium extract (He2) in preventing cognitive decline in a murine model of aging. We focused on recognition memory deterioration during aging, monitored through spontaneous behavioral tests assessing both memory components and frailty index. A parallel significant decrease in key markers of inflammation and oxidative stress, i.e., IL6, TGFβ1, GFAP, Nrf2, SOD1, COX2, NOS2, was revealed in the hippocampus by immunohistochemistry, accompanied by an enhancement of NMDAR1and mGluR2, crucially involved in glutamatergic neurotransmission. In summary, we disclosed a selective, preventive and neuroprotective effect of He2 on aged hippocampus, both on recognition memory as well on inflammation/oxidative stress/glutamate receptors expression.
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Shah J, Siddiquee MMR, Krell-Roesch J, Syrjanen JA, Kremers WK, Vassilaki M, Forzani E, Wu T, Geda YE. Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective. J Alzheimers Dis 2023; 92:1131-1146. [PMID: 36872783 PMCID: PMC11102734 DOI: 10.3233/jad-221261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.
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Affiliation(s)
- Jay Shah
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Md Mahfuzur Rahman Siddiquee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Janina Krell-Roesch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jeremy A. Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Walter K. Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Erica Forzani
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Yonas E. Geda
- Department of Neurology and the Franke Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA
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Chen X, Peng Y, Guo Y, Sun J, Li D, Cui J. MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation. Med Biol Eng Comput 2022; 60:3377-3395. [DOI: 10.1007/s11517-022-02673-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 09/17/2022] [Indexed: 11/11/2022]
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Chen T, Su ZH, Liu Z, Wang M, Cui ZF, Zhao L, Yang LJ, Zhang WC, Liu X, Liu J, Tan SY, Li SL, Feng QJ, Pang SM, Lu H. Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen. Orthop Surg 2022; 14:2256-2264. [PMID: 35979964 PMCID: PMC9483078 DOI: 10.1111/os.13431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level. METHODS A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m2 ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models. RESULTS The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05). CONCLUSION A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.
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Affiliation(s)
- Tao Chen
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhi-Hai Su
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zheng Liu
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Min Wang
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhi-Fei Cui
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Lei Zhao
- School of Biomedical Engineering, Southern Medical University, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Lian-Jun Yang
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Wei-Cong Zhang
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xiang Liu
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Jin Liu
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Shu-Yuan Tan
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Shao-Lin Li
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Qian-Jin Feng
- School of Biomedical Engineering, Southern Medical University, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
| | - Shu-Mao Pang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Hai Lu
- Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images. MATHEMATICS 2022. [DOI: 10.3390/math10122099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption.
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An Attention-Preserving Network-Based Method for Assisted Segmentation of Osteosarcoma MRI Images. MATHEMATICS 2022. [DOI: 10.3390/math10101665] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method.
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Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:171-182. [PMID: 34862541 DOI: 10.1007/978-3-030-85292-4_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.
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Pang S, Pang C, Su Z, Lin L, Zhao L, Chen Y, Zhou Y, Lu H, Feng Q. DGMSNet: Spine segmentation for MR image by a detection-guided mixed-supervised segmentation network. Med Image Anal 2021; 75:102261. [PMID: 34794095 DOI: 10.1016/j.media.2021.102261] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/09/2021] [Accepted: 10/04/2021] [Indexed: 11/29/2022]
Abstract
Spine segmentation for magnetic resonance (MR) images is important for various spinal diseases diagnosis and treatment, yet is still a challenge due to the inter-class similarity, i.e., shape and appearance similarities appear in neighboring spinal structures. To reduce inter-class similarity, existing approaches focus on enhancing the semantic information of spinal structures in the supervised segmentation network, whose generalization is limited by the size of pixel-level annotated dataset. In this paper, we propose a novel detection-guided mixed-supervised segmentation network (DGMSNet) to achieve automated spine segmentation. DGMSNet consists of a segmentation path for generating the spine segmentation prediction and a detection path (i.e., regression network) for producing heatmaps prediction of keypoints. A detection-guided learner in the detection path is introduced to generate a dynamic parameter, which is employed to produce a semantic feature map for segmentation path by adaptive convolution. A mixed-supervised loss comprised of a weighted combination of segmentation loss and detection loss is utilized to train DGMSNet with a pixel-level annotated dataset and a keypoints-detection annotated dataset. During training, a series of models are trained with various loss weights. In inference, a detection-guided label fusion approach is proposed to integrate the segmentation predictions generated by those trained models according to the consistency of predictions from the segmentation path and detection path. Experiments on T2-weighted MR images show that DGMSNet achieves the state-of-the-art performance with mean Dice similarity coefficients of 94.39% and 87.21% for segmentations of 5 vertebral bodies and 5 intervertebral discs on the in-house and public datasets respectively.
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Affiliation(s)
- Shumao Pang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China
| | - Chunlan Pang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Zhihai Su
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, China
| | - Liyan Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China
| | - Lei Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China
| | - Yangfan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China
| | - Yujia Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China
| | - Hai Lu
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China.
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Pang S, Pang C, Zhao L, Chen Y, Su Z, Zhou Y, Huang M, Yang W, Lu H, Feng Q. SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:262-273. [PMID: 32956047 DOI: 10.1109/tmi.2020.3025087] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.
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Zhu H, Tang Z, Cheng H, Wu Y, Fan Y. Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation. Sci Rep 2019; 9:16839. [PMID: 31727982 PMCID: PMC6856174 DOI: 10.1038/s41598-019-53387-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 10/30/2019] [Indexed: 01/15/2023] Open
Abstract
Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen’s d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer’s disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer’s disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).
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Affiliation(s)
- Hancan Zhu
- School of Mathematics Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China
| | - Zhenyu Tang
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China
| | - Hewei Cheng
- Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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