Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.
Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2025 - 2035 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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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=171950864&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950864 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950864 164039709 171950864 171950864 10.1007/s10278-023-00858-1 171950864 ppf: 2025 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images. aug: au: Zhang, Ke Luo, Guibo Li, Wenjuan Zhu, Yunfei Pan, Jielin Li, Ximeng Liu, Chaoran Liang, Jianchao Zhan, Yingying Zheng, Jing Li, Shaolin Cai, Wenli Hong, Guobin affil: https://ror.org/0064kty71 Department of Radiology, the Fifth Affiliated Hospital, Sun Yat-Sen University, 519000, Zhuhai, China sug: subj: Sacroiliitis Diagnosis Spondylitis, Ankylosing Diagnosis Spondylitis, Ankylosing Complications Image Processing, Computer Assisted Methods Convolutional Neural Networks Sacroiliac Joint Pathology Sacroiliac Joint Radiography Tomography, X-Ray Computed Automation Deep Learning Sacroiliitis Radiography Retrospective Design Radiologists Human Male Female Adult Middle Age Data Analysis Software Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0–I as class 0, grade II as class 1, and grades III–IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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