1
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Li X, Zhao F, Bai X, Wang X. Application value of cranial ultrasonography in quantitative evaluation of neonatal intracranial hemorrhage. Minerva Pediatr (Torino) 2024; 76:51-56. [PMID: 33182993 DOI: 10.23736/s2724-5276.20.05841-7] [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: 02/21/2024]
Abstract
BACKGROUND Intracranial hemorrhage is a severe cranial disease in the perinatal period. We aimed to explore the feasibility and accuracy of three-dimensional (3D) ultrasonography for the quantitative evaluation of neonatal intracranial hemorrhage. METHODS A total of 374 neonates with suspected intracranial hemorrhage from January 2017 to December 2019 were selected to be primarily screened by cranial ultrasonography and then diagnosed by cranial CT scan. The examination results were compared to analyze the feasibility and accuracy of 3D ultrasonography in quantifying blood loss. RESULTS CT scan showed that there were 102 cases of Papile grade I, 106 cases of grade II, 124 cases of grade III and 42 cases of grade IV. 3D ultrasonography showed that there were 108 cases of Papile grade I, 98 cases of grade II, 130 cases of grade III and 38 cases of grade IV. The diagnostic results of these two methods were not significantly different (P>0.05). The accuracies of CT scan for subventricular, intraventricular, subdural, subarachnoid and intraparenchymal hemorrhages were 47.33%, 31.24%, 94.62%, 91.73% and 91.35% respectively, and those of 3D ultrasonography were 98.74%, 96.37%, 91.51%, 90.41% and 97.64% respectively. The accuracies of 3D ultrasonography were significantly superior to those of CT scan for subependymal, intraventricular and intraparenchymal hemorrhages (P<0.05). CONCLUSIONS Neonatal intracranial hemorrhage can be well diagnosed by cranial ultrasonography which timely provides evidence for clinicians, thereby elevating the cure rate and reducing the mortality rate and incidence rate of sequelae. 3D ultrasonography is feasible and accurate for the quantitative evaluation of neonatal intracranial hemorrhage, thus being of great significance to prognostic determination in clinical practice.
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Affiliation(s)
- Xiujing Li
- Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Fangping Zhao
- Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Xiang Bai
- Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China -
| | - Xiang Wang
- Department of Ultrasonography, The Third Hospital of Chongqing Medical University, Chongqing, China
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2
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Sabeti M, Alikhani S, Shakoor M, Boostani R, Moradi E. Automatic determination of ventricular indices in hydrocephalic pediatric brain CT scan. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2022.101675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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3
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Zhou X, Ye Q, Yang X, Chen J, Ma H, Xia J, Del Ser J, Yang G. AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus. Neural Comput Appl 2022; 35:1-10. [PMID: 35228779 PMCID: PMC8866920 DOI: 10.1007/s00521-022-07048-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/16/2022]
Abstract
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Qinghao Ye
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA USA
| | - Xiaolin Yang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jiakun Chen
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Haiqin Ma
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen Second People’s Hospital, 3002 SunGang Road West, Shenzhen, 518035 Guangdong Province China
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Guang Yang
- Royal Brompton Hospital, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
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4
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Largent A, De Asis-Cruz J, Kapse K, Barnett SD, Murnick J, Basu S, Andersen N, Norman S, Andescavage N, Limperopoulos C. Automatic brain segmentation in preterm infants with post-hemorrhagic hydrocephalus using 3D Bayesian U-Net. Hum Brain Mapp 2022; 43:1895-1916. [PMID: 35023255 PMCID: PMC8933325 DOI: 10.1002/hbm.25762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 12/08/2021] [Accepted: 12/11/2021] [Indexed: 12/17/2022] Open
Abstract
Post‐hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two‐dimensional measurements of the ventricles. Automatic and reliable three‐dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U‐Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U‐Net, and ensemble learning using DenseNets and U‐Nets. A total of 41 T2‐weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4‐fold cross‐validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U‐Net, ensemble learning, and Bayesian U‐Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U‐Net were mean recall = 0.953 ± 0.037, mean negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U‐Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions.
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Affiliation(s)
- Axel Largent
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Kushal Kapse
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Scott D Barnett
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Jonathan Murnick
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Sudeepta Basu
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Nicole Andersen
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Stephanie Norman
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Nickie Andescavage
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA.,Department of Neonatology, Children's National Hospital, Washington, District of Columbia, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA.,Departments of Radiology and Pediatrics, George Washington University, Washington, District of Columbia, USA.,Neurology School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia, USA
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5
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Liu B, Liu S, Shang G, Chen Y, Wang Q, Niu X, Yang L, Zhang J. Direct 3D model extraction method for color volume images. Technol Health Care 2021; 29:133-140. [PMID: 33682753 PMCID: PMC8150494 DOI: 10.3233/thc-218014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.
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Affiliation(s)
- Bin Liu
- International School of Information Science and Engineering (DUT-RUISE), Dalian University of Technology, Dalian, Liaoning 116620, China.,DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian University of Technology, Dalian, Liaoning 116620, China.,Key Lab of Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, Liaoning 116620, China
| | - Shujun Liu
- International School of Information Science and Engineering (DUT-RUISE), Dalian University of Technology, Dalian, Liaoning 116620, China
| | - Guanning Shang
- Department of Orthopedic Surgery, ShengJing Hospital, China Medical University, Shengyang, Liaoning 110004, China
| | - Yanjie Chen
- International School of Information Science and Engineering (DUT-RUISE), Dalian University of Technology, Dalian, Liaoning 116620, China
| | - Qifeng Wang
- International School of Information Science and Engineering (DUT-RUISE), Dalian University of Technology, Dalian, Liaoning 116620, China
| | - Xiaolei Niu
- International School of Information Science and Engineering (DUT-RUISE), Dalian University of Technology, Dalian, Liaoning 116620, China
| | - Liang Yang
- The Second Hospital of Dalian Medical University, Dalian Medical University, Dalian, Liaoning 116023, China
| | - Jianxin Zhang
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China
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6
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Martin M, Sciolla B, Sdika M, Quétin P, Delachartre P. Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN. Comput Biol Med 2021; 131:104268. [PMID: 33639351 DOI: 10.1016/j.compbiomed.2021.104268] [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: 11/16/2020] [Revised: 02/09/2021] [Accepted: 02/09/2021] [Indexed: 10/22/2022]
Abstract
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Consequently, it must be detected and monitored by physicians. In clinical routing, manual 2D measurements are performed on 2D ultrasound (US) images to estimate the CVS volume but this practice is imprecise due to the unavailability of 3D information. A way to tackle this problem would be to develop automatic CVS segmentation algorithms for 3D US data. In this paper, we investigate the potential of 2D and 3D Convolutional Neural Networks (CNN) to solve this complex task and propose to use Compositional Pattern Producing Network (CPPN) to enable Fully Convolutional Networks (FCN) to learn CVS location. Our database was composed of 25 3D US volumes collected on 21 preterm nenonates at the age of 35.8±1.6 gestational weeks. We found that the CPPN enables to encode CVS location, which increases the accuracy of the CNNs when they have few layers. Accuracy of the 2D and 3D FCNs reached intraobserver variability (IOV) in the case of dilated ventricles with Dice of 0.893±0.008 and 0.886±0.004 respectively (IOV = 0.898±0.008) and with volume errors of 0.45±0.42 cm3 and 0.36±0.24 cm3 respectively (IOV = 0.41±0.05 cm3). 3D FCNs were more accurate than 2D FCNs in the case of normal ventricles with Dice of 0.797±0.041 against 0.776±0.038 (IOV = 0.816±0.009) and volume errors of 0.35±0.29 cm3 against 0.35±0.24 cm3 (IOV = 0.2±0.11 cm3). The best segmentation time of volumes of size 320×320×320 was obtained by a 2D FCN in 3.5±0.2 s.
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Affiliation(s)
- Matthieu Martin
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France.
| | - Bruno Sciolla
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
| | - Michaël Sdika
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
| | | | - Philippe Delachartre
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
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7
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Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G. Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Front Aging Neurosci 2020; 12:618538. [PMID: 33390930 PMCID: PMC7772233 DOI: 10.3389/fnagi.2020.618538] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/23/2020] [Indexed: 11/13/2022] Open
Abstract
Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework. Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning. Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9). Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
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Affiliation(s)
- Xi Zhou
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Qinghao Ye
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Yinghui Jiang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Minhao Wang
- Hangzhou Ocean's Smart Boya Co., Ltd., Hangzhou, China.,Mind Rank Ltd., Hongkong, China
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., London, United Kingdom
| | | | - Evandro Fei Fang
- Department of Clinical Molecular Biology, University of Oslo, Oslo, Norway
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Jun Xia
- Department of Radiology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
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8
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Jayaraman T, Reddy M S, Mahadevappa M, Sadhu A, Dutta PK. Modified distance regularized level set evolution for brain ventricles segmentation. Vis Comput Ind Biomed Art 2020; 3:29. [PMID: 33283254 PMCID: PMC7719594 DOI: 10.1186/s42492-020-00064-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 11/13/2020] [Indexed: 12/02/2022] Open
Abstract
Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.
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Affiliation(s)
- Thirumagal Jayaraman
- School of Medical Science and Technology, IIT Kharagpur, Kharagpur, 721302, India
| | - Sravan Reddy M
- Department of Electronics and Communications, JNTUA-College of Engineering, Pulivendula, 516390, India
| | | | - Anup Sadhu
- EKO CT & MRI Scan Centre, Medical College, Calcutta, 700073, India
| | - Pranab Kumar Dutta
- Department of Electrical Engineering, IIT Kharagpur, Kharagpur, 721302, India
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9
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Quon JL, Han M, Kim LH, Koran ME, Cheng LC, Lee EH, Wright J, Ramaswamy V, Lober RM, Taylor MD, Grant GA, Cheshier SH, Kestle JRW, Edwards MS, Yeom KW. Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. J Neurosurg Pediatr 2020; 27:131-138. [PMID: 33260138 PMCID: PMC9707365 DOI: 10.3171/2020.6.peds20251] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 06/10/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. METHODS The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. RESULTS Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). CONCLUSIONS The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.
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Affiliation(s)
- Jennifer L. Quon
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
| | - Michelle Han
- Stanford University School of Medicine, Stanford, California
| | - Lily H. Kim
- Stanford University School of Medicine, Stanford, California
| | - Mary Ellen Koran
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Leo C. Cheng
- Department of Urology, Stanford University School of Medicine, Stanford, California
| | - Edward H. Lee
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Jason Wright
- Department of Radiology, Seattle Children’s Hospital, University of Washington School of Medicine, Seattle, Washington
| | - Vijay Ramaswamy
- Department of Neurosurgery, The Hospital for Sick Children, University of Toronto, Ontario, Canada
| | - Robert M. Lober
- Department of Neurosurgery, Dayton Children’s Hospital, Wright State University Boonshoft School of Medicine, Dayton, Ohio
| | - Michael D. Taylor
- Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah
| | - Gerald A. Grant
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
| | - Samuel H. Cheshier
- Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah
| | - John R. W. Kestle
- Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah
| | - Michael S.B. Edwards
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
| | - Kristen W. Yeom
- Division of Pediatric Neurosurgery, Lucile Packard Children’s Hospital, Stanford, California
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10
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Neikter J, Agerskov S, Hellström P, Tullberg M, Starck G, Ziegelitz D, Farahmand D. Ventricular Volume Is More Strongly Associated with Clinical Improvement Than the Evans Index after Shunting in Idiopathic Normal Pressure Hydrocephalus. AJNR Am J Neuroradiol 2020; 41:1187-1192. [PMID: 32527841 DOI: 10.3174/ajnr.a6620] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 04/27/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Ventricular enlargement in idiopathic normal pressure hydrocephalus is often estimated using the Evans index. However, the sensitivity of the Evans index to estimate changes in ventricular size postoperatively has been questioned. Here, we evaluated the postoperative change in ventricle size in relation to shunt response in patients with idiopathic normal pressure hydrocephalus, by comparing ventricular volume and the Evans index. MATERIALS AND METHODS Fifty-seven patients with idiopathic normal pressure hydrocephalus underwent high-resolution MR imaging preoperatively and 6 months after shunt insertion. Clinical symptoms of gait, balance, cognition, and continence were assessed according to the idiopathic normal pressure hydrocephalus scale. The ventricular volume of the lateral and third ventricles and the Evans index were measured using ITK-SNAP software. Semiautomatic volumetric analysis was performed, and postoperative changes in ventricular volume and the Evans index and their relationships to postoperative clinical improvement were compared. RESULTS The median postoperative ventricular volume decrease was 25 mL (P < .001). The proportional decrease in ventricular volume was greater than that in the Evans index (P < .001). The postoperative decrease in ventricular volume was associated with a postoperative increase in the idiopathic normal pressure hydrocephalus scale score (P = .004). Shunt responders (75%) demonstrated a greater ventricular volume decrease than nonresponders (P = .002). CONCLUSIONS Clinical improvement after shunt surgery in idiopathic normal pressure hydrocephalus is associated with a reduction of ventricular size. Ventricular volume is a more sensitive estimate than the Evans index and, therefore, constitutes a more precise method to evaluate change in ventricle size after shunt treatment in idiopathic normal pressure hydrocephalus.
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Affiliation(s)
- J Neikter
- From the Department of Clinical Neuroscience (J.N., S.A., P.H., M.T., D.F.)
| | - S Agerskov
- From the Department of Clinical Neuroscience (J.N., S.A., P.H., M.T., D.F.)
| | - P Hellström
- From the Department of Clinical Neuroscience (J.N., S.A., P.H., M.T., D.F.)
| | - M Tullberg
- From the Department of Clinical Neuroscience (J.N., S.A., P.H., M.T., D.F.)
| | - G Starck
- Institute of Neuroscience and Physiology, Hydrocephalus Research Unit, and Departments of Radiation Physics (G.S.)
| | - D Ziegelitz
- Neuroradiology (D.Z.), Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - D Farahmand
- From the Department of Clinical Neuroscience (J.N., S.A., P.H., M.T., D.F.)
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11
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Najm M, Kuang H, Federico A, Jogiat U, Goyal M, Hill MD, Demchuk A, Menon BK, Qiu W. Automated brain extraction from head CT and CTA images using convex optimization with shape propagation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:1-8. [PMID: 31200897 DOI: 10.1016/j.cmpb.2019.04.030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/20/2019] [Accepted: 04/28/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Non-Contrast Computer Tomography (NCCT) and CT angiography (CTA) are the most used and widely acceptable imaging modalities in clinical practice for the diagnosis and treatment of acute ischemic stroke (AIS) patients. Brain extraction of CT/CTA images plays an essential role in stroke imaging research. There is no robust automated brain extraction method in the literature that is well established for both NCCT and CTA images. Thus, a validated and automated brain extraction tool for CT imaging would be of great value for both research and clinical practice. METHODS The proposed brain extraction method is based on the contour evolution technique, which extracts brain tissues from acquired NCCT and CTA images in a slice-by-slice fashion. Specifically, the proposed approach makes use of a novel propagation framework, which is initialized by a localized slice with the largest brain section in axial views, followed by a geodesic level-set evolution for automatically extracting the brain section in each slice. In particular, the segmented contour propagated from the previous slice is reused to penalize the defined object function for contour evolution to enforce the shape continuity between any two adjacent contours. We show that the defined contour evolution function can be solved iteratively by globally optimal convex optimization. RESULTS The proposed brain extraction approach is quantitatively evaluated using 40 NCCT and CTA images acquired from 20 AIS patients and drawn from 4 different vendors, compared to manual segmentations using Dice and Jaccard coefficient metrics. The quantitative results show that the proposed segmentation algorithm is consistently accurate for both NCCT and CTA images using Dice metric. The proposed method is further validated on 1736 NCCT and CTA images of 1331 AIS patients acquired from three multi-national multi-centric clinical trials. A visual check performed on these data demonstrates a low failure rate of 0.4% for 1331 NCCT images and a zero-failure rate for 405 CTA images. CONCLUSIONS Both quantitative and qualitative evaluation suggest that the proposed brain extraction approach for NCCT and CTA images can be used for different clinical imaging settings, thus serving to improve current image analysis in the field of neuroimaging.
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Affiliation(s)
- Mohamed Najm
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Hulin Kuang
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Alyssa Federico
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Uzair Jogiat
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Mayank Goyal
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Michael D Hill
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Andrew Demchuk
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Bijoy K Menon
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Wu Qiu
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada.
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Joint Segmentation of Intracerebral Hemorrhage and Infarct from Non-Contrast CT Images of Post-treatment Acute Ischemic Stroke Patients. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/978-3-030-00931-1_78] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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13
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Lindberg K, Kouti A, Ziegelitz D, Hallén T, Skoglund T, Farahmand D. Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations. J Neurol Surg B Skull Base 2018; 79:475-481. [PMID: 30210975 DOI: 10.1055/s-0037-1618577] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 12/01/2017] [Indexed: 10/18/2022] Open
Abstract
Background The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as abc/2 (simplified ellipsoid volume equation) and 4πr 3 /3 (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation. The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs. Methods Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with abc/2 and 4πr 3 /3 by a clinician, and the results were compared with the corresponding segmented volumes. Results There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with abc/2 ( p < 0.001, mean volume 18% higher than segmentation) and 4πr 3 /3 ( p < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99. Conclusion CGE ( abc/2 and 4πr 3 /3 ) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.
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Affiliation(s)
- Karl Lindberg
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Neuroradiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Angelica Kouti
- Department of Neuroradiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Doerthe Ziegelitz
- Department of Neuroradiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Tobias Hallén
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Thomas Skoglund
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dan Farahmand
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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14
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Lu X, Yang R, Xie Q, Ou S, Zha Y, Wang D. Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images. Biomed Eng Online 2017; 16:39. [PMID: 28351368 PMCID: PMC5370472 DOI: 10.1186/s12938-017-0323-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 02/10/2017] [Indexed: 12/01/2022] Open
Abstract
Background Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. Methods In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. Results We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 5.0 \times 10^{ - 4} $$\end{document}p=5.0×10-4) for left ventricle myocardium and from 0.6307 to 0.6519 (\documentclass[12pt]{minimal}
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\begin{document}$$ p = 6.0 \times 10^{ - 4} $$\end{document}p=6.0×10-4) for right atrium. Conclusions The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.
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Affiliation(s)
- Xuesong Lu
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Rongqian Yang
- School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
| | - Qinlan Xie
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China
| | - Shanxing Ou
- Radiology Department, Guangzhou General Hospital of Guangzhou Military Area Command, Guangzhou, 510010, People's Republic of China
| | - Yunfei Zha
- Department of Radiology, Remin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| | - Defeng Wang
- Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. .,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
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15
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Rebouças Filho PP, Cortez PC, da Silva Barros AC, C Albuquerque VH, R S Tavares JM. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal 2016; 35:503-516. [PMID: 27614793 DOI: 10.1016/j.media.2016.09.002] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 08/31/2016] [Accepted: 09/02/2016] [Indexed: 10/21/2022]
Abstract
The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.
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Affiliation(s)
- Pedro Pedrosa Rebouças Filho
- Laboratório de Processamento de Imagens e Simulação Computacional, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Maracanau, CE, Brazil.
| | - Paulo César Cortez
- Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza, CE, Brazil.
| | - Antônio C da Silva Barros
- Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza, Fortaleza, Ceará, Brazil.
| | - Victor Hugo C Albuquerque
- Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza, Fortaleza, Ceará, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
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