An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.
Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2216 - 2227 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515401&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515401 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515401 181515401 181515401 10.1007/s10278-024-01091-0 181515401 ppf: 2216 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images. aug: au: Saeed, Muhammad Usman Bin, Wang Sheng, Jinfang Mobarak Albarakati, Hussain affil: https://ror.org/00f1zfq44 School of Computer Science and Engineering, Central South University, 410083, Changsha, Hunan, China sug: subj: Spinal Fractures Radiography Tomography, X-Ray Computed Automation Deep Learning Methods Human Experimental Studies Descriptive Statistics Sensitivity and Specificity Image Interpretation, Computer Assisted Methods ab: Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules are used to apply multi-scale feature fusion, spatial feature extraction, channel-wise feature improvement, segmentation border results border refinement, and positional focus on the region of interest. After that, a decoder network is used to predict the fractured spine. The experimental results show that the proposed approach achieves better accuracy results in solving the above challenges and also performs well compared to the existing segmentation methods. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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