Automatic Lumbar MRI Detection and Identification Based on Deep Learning.
The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A n...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 3; pp. 513 - 521 |
|---|---|
| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
|
| 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=136223481&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223481 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2019 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223481 136223481 136223481 10.1007/s10278-018-0130-7 136223481 ppf: 513 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Lumbar MRI Detection and Identification Based on Deep Learning. aug: au: Zhou, Yujing Liu, Yuan Chen, Qian Gu, Guohua Sui, Xiubao affil: The School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, 200 Xiaolingwei Road, Xuanwu Region, 210094, Nanjing, Jiangsu, China sug: subj: Lumbar Vertebrae Magnetic Resonance Imaging Deep Learning Human Algorithms Neural Networks (Computer) ab: The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image. In the convolutional neural network, a contrast object will not update during frames, which allows a fast speed and saves memory. Due to its distinctive shape, S1 is firstly detected and a rough region around it is extracted for searching for L1–L5. The results are evaluated with accuracy, precision, mean, and standard deviation (STD). Finally, our detection algorithm achieves the accuracy of 98.6% and the precision of 98.9%. Most failed results are involved with wrong S1 locations or missed L5. The study demonstrates that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images. It can be believed that our detection method will assist clinicians to raise working efficiency. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|