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Liu K, Li P, Otikovs M, Ning X, Xia L, Wang X, Yang L, Pan F, Zhang Z, Wu G, Xie H, Bao Q, Zhou X, Liu C. Mutually communicated model based on multi-parametric MRI for automated segmentation and classification of prostate cancer. Med Phys 2023. [PMID: 36905102 DOI: 10.1002/mp.16343] [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: 08/27/2022] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mp-MRI) is introduced and established as a noninvasive alternative for prostate cancer (PCa) detection and characterization. PURPOSE To develop and evaluate a mutually communicated deep learning segmentation and classification network (MC-DSCN) based on mp-MRI for prostate segmentation and PCa diagnosis. METHODS The proposed MC-DSCN can transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way. For classification task, the MC-DSCN can transfer the masks produced by the coarse segmentation component to the classification component to exclude irrelevant regions and facilitate classification. For segmentation task, this model can transfer the high-quality localization information learned by the classification component to the fine segmentation component to mitigate the impact of inaccurate localization on segmentation results. Consecutive MRI exams of patients were retrospectively collected from two medical centers (referred to as center A and B). Two experienced radiologists segmented the prostate regions, and the ground truth of the classification refers to the prostate biopsy results. MC-DSCN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted and apparent diffusion coefficient) and the effect of different architectures on the network's performance was tested and discussed. Data from center A were used for training, validation, and internal testing, while another center's data were used for external testing. The statistical analysis is performed to evaluate the performance of the MC-DSCN. The DeLong test and paired t-test were used to assess the performance of classification and segmentation, respectively. RESULTS In total, 134 patients were included. The proposed MC-DSCN outperforms the networks that were designed solely for segmentation or classification. Regarding the segmentation task, the classification localization information helped to improve the IOU in center A: from 84.5% to 87.8% (p < 0.01) and in center B: from 83.8% to 87.1% (p < 0.01), while the area under curve (AUC) of PCa classification was improved in center A: from 0.946 to 0.991 (p < 0.02) and in center B: from 0.926 to 0.955 (p < 0.01) as a result of the additional information provided by the prostate segmentation. CONCLUSION The proposed architecture could effectively transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way, thus outperforming the networks designed to perform only one task.
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Affiliation(s)
- Kewen Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Piqiang Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Martins Otikovs
- Weizmann Institute of Science, Department of Chemical and Biological Physics, Rehovot, Israel
| | - Xinzhou Ning
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Liyang Xia
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Xiangyu Wang
- First Affiliated Hospital of Shenzhen University, Shenzhen, P.R. China
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Feng Pan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Zhi Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Guangyao Wu
- Shenzhen University General Hospital, Shenzhen, P.R. China
| | - Han Xie
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Qingjia Bao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,University of Chinese Academy of Sciences, Beijing, P.R. China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology-Optics Valley Laboratory, Wuhan, P.R. China
| | - Chaoyang Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,University of Chinese Academy of Sciences, Beijing, P.R. China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology-Optics Valley Laboratory, Wuhan, P.R. China
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Nasir ES, Parvaiz A, Fraz MM. Nuclei and glands instance segmentation in histology images: a narrative review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10372-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Choi EY, Tian L, Su JH, Radovan MT, Tourdias T, Tran TT, Trelle AN, Mormino E, Wagner AD, Rutt BK. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage 2022; 262:119584. [PMID: 36007822 PMCID: PMC9787236 DOI: 10.1016/j.neuroimage.2022.119584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 08/09/2022] [Accepted: 08/21/2022] [Indexed: 02/02/2023] Open
Abstract
The thalamus is a central integration structure in the brain, receiving and distributing information among the cerebral cortex, subcortical structures, and the peripheral nervous system. Prior studies clearly show that the thalamus atrophies in cognitively unimpaired aging. However, the thalamus is comprised of multiple nuclei involved in a wide range of functions, and the age-related atrophy of individual thalamic nuclei remains unknown. Using a recently developed automated method of identifying thalamic nuclei (3T or 7T MRI with white-matter-nulled MPRAGE contrast and THOMAS segmentation) and a cross-sectional design, we evaluated the age-related atrophy rate for 10 thalamic nuclei (AV, CM, VA, VLA, VLP, VPL, pulvinar, LGN, MGN, MD) and an epithalamic nucleus (habenula). We also used T1-weighted images with the FreeSurfer SAMSEG segmentation method to identify and measure age-related atrophy for 11 extra-thalamic structures (cerebral cortex, cerebral white matter, cerebellar cortex, cerebellar white matter, amygdala, hippocampus, caudate, putamen, nucleus accumbens, pallidum, and lateral ventricle). In 198 cognitively unimpaired participants with ages spanning 20-88 years, we found that the whole thalamus atrophied at a rate of 0.45% per year, and that thalamic nuclei had widely varying age-related atrophy rates, ranging from 0.06% to 1.18% per year. A functional grouping analysis revealed that the thalamic nuclei involved in cognitive (AV, MD; 0.53% atrophy per year), visual (LGN, pulvinar; 0.62% atrophy per year), and auditory/vestibular (MGN; 0.64% atrophy per year) functions atrophied at significantly higher rates than those involved in motor (VA, VLA, VLP, and CM; 0.37% atrophy per year) and somatosensory (VPL; 0.32% atrophy per year) functions. A proximity-to-CSF analysis showed that the group of thalamic nuclei situated immediately adjacent to CSF atrophied at a significantly greater atrophy rate (0.59% atrophy per year) than that of the group of nuclei located farther from CSF (0.36% atrophy per year), supporting a growing hypothesis that CSF-mediated factors contribute to neurodegeneration. We did not find any significant hemispheric differences in these rates of change for thalamic nuclei. Only the CM thalamic nucleus showed a sex-specific difference in atrophy rates, atrophying at a greater rate in male versus female participants. Roughly half of the thalamic nuclei showed greater atrophy than all extra-thalamic structures examined (0% to 0.54% per year). These results show the value of white-matter-nulled MPRAGE imaging and THOMAS segmentation for measuring distinct thalamic nuclei and for characterizing the high and heterogeneous atrophy rates of the thalamus and its nuclei across the adult lifespan. Collectively, these methods and results advance our understanding of the role of thalamic substructures in neurocognitive and disease-related changes that occur with aging.
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Affiliation(s)
- Eun Young Choi
- Department of Neurosurgery, Stanford University, 300 Pasteur Drive, MC5327, Stanford, CA 94305, USA
| | - Lu Tian
- Department of Biomedical Data Science, 1265 Welch Road, MC5464, Stanford, CA 94305, USA
| | - Jason H. Su
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, MC9505, Stanford, CA 94305, USA
| | - Matthew T. Radovan
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, MC9025, Stanford, CA 94305, USA
| | - Thomas Tourdias
- Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France,INSERM U1215, Neurocentre Magendie, University of Bordeaux, France
| | - Tammy T. Tran
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Alexandra N. Trelle
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford, University, 300 Pasteur Drive, MC5235, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Anthony D. Wagner
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Brian K. Rutt
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA,Corresponding author. (B.K. Rutt)
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Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI. Neuroinformatics 2022; 20:651-664. [PMID: 34626333 PMCID: PMC8993941 DOI: 10.1007/s12021-021-09544-5] [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] [Accepted: 08/19/2021] [Indexed: 12/31/2022]
Abstract
Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming. In this work, we propose a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images. Transformation of images to an efficient representation has been proposed to improve the performance of subsequent classification tasks especially when working with limited labeled data. We investigate this by transforming the MPRAGE images to White-Matter-nulled MPRAGE (WMn-MPRAGE) contrast, previously shown to exhibit good intra-thalamic nuclear contrast, prior to the segmentation step. We trained two 3D segmentation frameworks using MPRAGE images (n = 35 subjects): (a) a native contrast segmentation (NCS) on MPRAGE images and (b) a synthesized contrast segmentation (SCS) where synthesized WMn-MPRAGE representation generated by a contrast synthesis CNN were used. Thalamic nuclei labels were generated using THOMAS, a multi-atlas segmentation technique proposed for WMn-MPRAGE images. The segmentation accuracy and clinical utility were evaluated on a healthy cohort (n = 12) and a cohort (n = 45) comprising of healthy subjects and patients with alcohol use disorder (AUD), respectively. Both the segmentation CNNs yielded comparable performances on most thalamic nuclei with Dice scores greater than 0.84 for larger nuclei and at least 0.7 for smaller nuclei. However, for some nuclei, the SCS CNN yielded significant improvements in Dice scores (medial geniculate nucleus, P = 0.003, centromedian nucleus, P = 0.01) and percent volume difference (ventral anterior, P = 0.001, ventral posterior lateral, P = 0.01) over NCS. In the AUD cohort, the SCS CNN demonstrated a significant atrophy in ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P = 0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS CNN showed spurious atrophy of the ventral posterior lateral nucleus. CNN-based segmentation of thalamic nuclei provides a fast and automated technique for thalamic nuclei prediction in MPRAGE images. The transformation of images to an efficient representation, such as WMn-MPRAGE, can provide further improvements in segmentation performance.
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Boelens Keun JT, van Heese EM, Laansma MA, Weeland CJ, de Joode NT, van den Heuvel OA, Gool JK, Kasprzak S, Bright JK, Vriend C, van der Werf YD. Structural assessment of thalamus morphology in brain disorders: A review and recommendation of thalamic nucleus segmentation and shape analysis. Neurosci Biobehav Rev 2021; 131:466-478. [PMID: 34587501 DOI: 10.1016/j.neubiorev.2021.09.044] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 08/25/2021] [Accepted: 09/24/2021] [Indexed: 12/30/2022]
Abstract
The thalamus is a central brain structure crucially involved in cognitive, emotional, sensory, and motor functions and is often reported to be involved in the pathophysiology of neurological and psychiatric disorders. The functional subdivision of the thalamus warrants morphological investigation on the level of individual subnuclei. In addition to volumetric measures, the investigation of other morphological features may give additional insights into thalamic morphology. For instance, shape features offer a higher spatial resolution by revealing small, regional differences that are left undetected in volumetric analyses. In this review, we discuss the benefits and limitations of recent advances in neuroimaging techniques to investigate thalamic morphology in vivo, leading to our proposed methodology. This methodology consists of available pipelines for volume and shape analysis, focussing on the morphological features of volume, thickness, and surface area. We demonstrate this combined approach in a Parkinson's disease cohort to illustrate their complementarity. Considering our findings, we recommend a combined methodology as it allows for more sensitive investigation of thalamic morphology in clinical populations.
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Affiliation(s)
- Jikke T Boelens Keun
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Eva M van Heese
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Max A Laansma
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Cees J Weeland
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Niels T de Joode
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Jari K Gool
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; SEIN, Heemstede, the Netherlands; Department of Neurology, LUMC, Leiden, the Netherlands
| | - Selina Kasprzak
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Joanna K Bright
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Ysbrand D van der Werf
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands.
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The specificity of thalamic alterations in Korsakoff's syndrome: Implications for the study of amnesia. Neurosci Biobehav Rev 2021; 130:292-300. [PMID: 34454914 DOI: 10.1016/j.neubiorev.2021.07.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 04/01/2021] [Accepted: 07/15/2021] [Indexed: 02/07/2023]
Abstract
The pathophysiological mechanisms behind amnesia are still unknown. Recent literature, through the study of patients with Alcohol Use Disorder with and without Korsakoff's syndrome, increasingly shows that physiological alterations to the thalamus have an important role in the development of amnesia. This review gives an overview of neuropsychological, neuropathological and neuroimaging contributions to the understanding of Korsakoff's syndrome, highlighting the central role of the thalamus in this amnesia. The thalamus being a multi-nucleus structure, the limitations regarding the loci, nature and alterations to specific nuclei are discussed, along with potential solutions. Finally, future directions for clinical research are laid out to unravel the intricacies inherent to amnesia. They consider the need to evaluate the physiological role of the thalamus, not only as an entity but also as part of a brain circuit through a more integrative approach.
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