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Zhang Z, Zhao Y, Chou D, Zhang S, Zhou R, Ma Z, Wang L, Yu Z, Liu Y, Wang Y. Study on articular surface morphology of atlantoaxial lateral mass based on differential manifold. J Orthop Surg Res 2023; 18:919. [PMID: 38042858 PMCID: PMC10693051 DOI: 10.1186/s13018-023-04410-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 11/26/2023] [Indexed: 12/04/2023] Open
Abstract
OBJECTIVES To propose a surface reconstruction algorithm based on a differential manifold (a space with local Euclidean space properties), which can be used for processing of clinical images and for modeling of the atlantoaxial joint. To describe the ideal anatomy of the lateral atlantoaxial articular surface by measuring the anatomical data. METHODS Computed tomography data of 80 healthy subjects who underwent cervical spine examinations at our institution were collected between October 2019 and June 2022, including 46 males and 34 females, aged 37.8 ± 5.1 years (28-59 years). A differential manifold surface reconstruction algorithm was used to generate the model based on DICOM data derived by Vision PACS system. The lateral mass articular surface was measured and compared in terms of its sagittal diameter, transverse diameter, articular surface area, articular curvature and joint space height. RESULTS There was no statistically significant difference between left and right sides of the measured data in normal adults (P > 0.05). The atlantoaxial articular surface sagittal diameter length was (15.83 ± 1.85) and (16.22 ± 1.57) mm on average, respectively. The transverse diameter length of the articular surface was (16.29 ± 2.16) and (16.49 ± 1.84) mm. The lateral articular surface area was (166.53 ± 7.69) and (174.48 ± 6.73) mm2 and the curvature was (164.03 ± 5.27) and (153.23 ± 9.03)°, respectively. The joint space height was 3.05 ± 0.11mm, respectively. There is an irregular articular space in the lateral mass of atlantoaxial, and both upper and lower surfaces of the articular space are concave. A sagittal plane view shows that the inferior articular surface of the atlas is mainly concave above; however, the superior articular surface of the axis is mainly convex above. In the coronal plane, the inferior articular surface of the atlas is mostly concave above, with most concave vertices located in the medial region, and the superior articular surface of the axis is mainly concave below, with most convex vertices located centrally and laterally. CONCLUSION A differential manifold algorithm can effectively process atlantoaxial imaging data, fit and control mesh topology, and reconstruct curved surfaces to meet clinical measurement applications with high accuracy and efficiency; the articular surface of the lateral mass of atlantoaxial mass in normal adults has relatively constant sagittal diameter, transverse diameter and area. The distance difference between joint spaces is small, but the shape difference of articular surfaces differs greatly.
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Affiliation(s)
- Zeyuan Zhang
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yao Zhao
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China
| | - Dean Chou
- Department of the Neurosurgery, Columbia University, New York, USA
| | - Shuhao Zhang
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China
| | - Ruifang Zhou
- School of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou, China
| | - Zeyu Ma
- School of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou, China
| | - Limin Wang
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China
| | - Zhong Yu
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yilin Liu
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China.
| | - Yuqiang Wang
- Department of the Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450052, China.
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Ng SMS, Low R, Pak C, Lai S, Lee B, McCluskey P, Symes R, Invernizzi A, Tsui E, Sitaula RK, Kharel M, Khatri A, Utami AN, La Distia Nora R, Putera I, Sen A, Agarwal M, Mahendradas P, Biswas J, Pavesio C, Cimino L, Sobrin L, Kempen JH, Gupta V, Agrawal R. The role of a multicentre data repository in ocular inflammation: The Ocular Autoimmune Systemic Inflammatory Infectious Study (OASIS). Eye (Lond) 2023; 37:3084-3096. [PMID: 36918629 PMCID: PMC10564879 DOI: 10.1038/s41433-023-02472-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
In the current literature, clinical registry cohorts related to ocular inflammation are few and far between, and there are none involving multi-continental international data. Many existing registries comprise administrative databases, data related to specific uveitic diseases, or are designed to address a particular clinical problem. The existing data, although useful and serving their intended purposes, are segmented and may not be sufficiently robust to design prognostication tools or draw epidemiological conclusions in the field of uveitis and ocular inflammation. To solve this, we have developed the Ocular Autoimmune Systemic Inflammatory Infectious Study (OASIS) Clinical Registry. OASIS collects prospective and retrospective data on patients with all types of ocular inflammatory conditions from centers all around the world. It is a primarily web-based platform with alternative offline modes of access. A comprehensive set of clinical data ranging from demographics, past medical history, clinical presentation, working diagnosis to visual outcomes are collected over a range of time points. Additionally, clinical images such as optical coherence tomography, fundus fluorescein angiography and indocyanine green angiography studies may be uploaded. Through the capturing of diverse, well-structured, and clinically meaningful data in a simplified and consistent fashion, OASIS will deliver a comprehensive and well organized data set ripe for data analysis. The applications of the registry are numerous, and include performing epidemiological analysis, monitoring drug side effects, and studying treatment safety efficacy. Furthermore, the data compiled in OASIS will be used to develop new classification and diagnostic systems, as well as treatment and prognostication guidelines for uveitis.
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Affiliation(s)
- Sean Ming Sheng Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Rebecca Low
- Department of Ophthalmology, National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Clara Pak
- University of Rochester School of Medicine & Dentistry, Rochester, NY, USA
| | - SerSei Lai
- Department of Ophthalmology, National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Bernett Lee
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Peter McCluskey
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia
| | - Richard Symes
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia
| | - Alessandro Invernizzi
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia
- Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco," Luigi Sacco Hospital, University of Milan, Milan, Italy
| | - Edmund Tsui
- Stein Eye Institute, David Geffen of Medicine at UCLA, Los Angeles, CA, USA
| | - Ranju Kharel Sitaula
- Department of Ophthalmology, B. P. Koirala Lions Centre for Ophthalmic Studies, Institute of Medicine, Tribhuvan University, Kathmandu, Nepal
| | - Muna Kharel
- Nepal Army Institute of Health Sciences, Kathmandu, Nepal
| | | | | | | | | | - Alok Sen
- Sadguru Netra Chikitsalaya, Chitrakoot, Madhya Pradesh, India
| | - Manisha Agarwal
- Department of Ophthalmology, Dr Shroff's Charity Eye Hospital Daryaganj, New Delhi, India
| | | | | | - Carlos Pavesio
- Moorfields Eye Hospital, NHS Foundation Trust, London, UK
| | - Luca Cimino
- Department of Surgery, Medicine Dentistry and Morphological Sciences with Interest in Transplant, University of Modena and Reggio Emilia, Modena, Italy
- Ocular Immunology Unit, Azienda USL-IRCCS di Reggio Emilia, 42121, Reggio Emilia, Italy
| | - Lucia Sobrin
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary and Harvard Medical School, Schepens Eye Research Institute, Boston, MA, USA
| | - John H Kempen
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary and Harvard Medical School, Schepens Eye Research Institute, Boston, MA, USA
- MyungSung Christian Medical Center (MCM) Eye Unit, MCM General Hospital and MyungSung Medical School, Addis Ababa, Ethiopia
- Department of Ophthalmology, Addis Ababa University Faculty of Medicine, Addis Ababa, Ethiopia
- Sight for Souls, Fort Myers, FL, USA
| | - Vishali Gupta
- Advanced Eye Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Rupesh Agrawal
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- Department of Ophthalmology, National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore.
- Moorfields Eye Hospital, NHS Foundation Trust, London, UK.
- Singapore Eye Research Institute, The Academia, Singapore, Singapore.
- Department of Ophthalmology and Visual Sciences, Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.
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Alvarez-Meza AM, Orozco-Gutierrez A, Castellanos-Dominguez G. Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns. Front Neurosci 2017; 11:550. [PMID: 29056897 PMCID: PMC5635061 DOI: 10.3389/fnins.2017.00550] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 09/20/2017] [Indexed: 11/13/2022] Open
Abstract
We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.
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Zimmer VA, Glocker B, Hahner N, Eixarch E, Sanroma G, Gratacós E, Rueckert D, González Ballester MÁ, Piella G. Learning and combining image neighborhoods using random forests for neonatal brain disease classification. Med Image Anal 2017; 42:189-199. [PMID: 28818743 DOI: 10.1016/j.media.2017.08.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 08/01/2017] [Accepted: 08/08/2017] [Indexed: 12/25/2022]
Abstract
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defined distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning.
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Affiliation(s)
| | - Ben Glocker
- BioMedIA Group, Imperial College London, London, UK
| | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | | | - Eduard Gratacós
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | | | | | - Gemma Piella
- SIMBioSys, Universitat Pompeu Fabra, Barcelona, Spain
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