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Jian M, Wu R, Xu W, Zhi H, Tao C, Chen H, Li X. VascuConNet: an enhanced connectivity network for vascular segmentation. Med Biol Eng Comput 2024:10.1007/s11517-024-03150-8. [PMID: 38898202 DOI: 10.1007/s11517-024-03150-8] [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: 11/22/2023] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.
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Affiliation(s)
- Muwei Jian
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
- School of Information Science and Technology, Linyi University, Linyi, China.
| | - Ronghua Wu
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Wenjin Xu
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Huixiang Zhi
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Chen Tao
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Hongyu Chen
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Xiaoguang Li
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China.
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A Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Tracking. Diagnostics (Basel) 2022; 12:diagnostics12040935. [PMID: 35453985 PMCID: PMC9031684 DOI: 10.3390/diagnostics12040935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 02/01/2023] Open
Abstract
Glaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological examination, using images of the optic papilla obtained in vivo for the evaluation of glaucomatous characteristics, eye pressure, and visual field. Identifying the glaucomatous papilla is quite important, as optical papillary images are considered the gold standard for tracking. Therefore, this article presents a review of the diagnostic methods used to identify the glaucomatous papilla through technology over the last five years. Based on the analyzed works, the current state-of-the-art methods are identified, the current challenges are analyzed, and the shortcomings of these methods are investigated, especially from the point of view of automation and independence in performing these measurements. Finally, the topics for future work and the challenges that need to be solved are proposed.
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Faura G, Boix-Lemonche G, Holmeide AK, Verkauskiene R, Volke V, Sokolovska J, Petrovski G. Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning. SENSORS 2022; 22:s22030718. [PMID: 35161465 PMCID: PMC8839630 DOI: 10.3390/s22030718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/20/2021] [Accepted: 12/26/2021] [Indexed: 12/13/2022]
Abstract
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.
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Affiliation(s)
- Georgina Faura
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Medical Biochemistry, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Gerard Boix-Lemonche
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
| | | | - Rasa Verkauskiene
- Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, LT-50009 Kaunas, Lithuania;
| | - Vallo Volke
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia;
- Institute of Biomedical and Transplant Medicine, Department of Medical Sciences, Tartu University Hospital, L. Puusepa Street, 51014 Tartu, Estonia
| | | | - Goran Petrovski
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Ophthalmology, Oslo University Hospital, 0450 Oslo, Norway
- Correspondence: ; Tel.: +47-9222-6158
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