Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics.
Vertebral Compression Fracture (VCF) occurs when the vertebral body partially collapses under the action of compressive forces. Non-traumatic VCFs can be secondary to osteoporosis fragility (benign VCFs) or tumors (malignant VCFs). The investigation of the etiology of non-traumatic VCFs is usually n...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 446 - 459 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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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=157184678&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184678 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184678 155108109 157184678 157184678 10.1007/s10278-022-00586-y 157184678 ppf: 446 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics. aug: au: Del Lama, Rafael Silva Candido, Raquel Mariana Chiari-Correia, Natália Santana Nogueira-Barbosa, Marcello Henrique de Azevedo-Marques, Paulo Mazzoncini Tinós, Renato affil: Department of Computing and Mathematics, FFCLRP, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, Brazil sug: subj: Diagnosis, Computer Assisted Fractures, Vertebral Compression Diagnosis Fractures, Vertebral Compression Classification Neural Networks (Computer) Diagnostic Imaging Clinical Information Systems Human Algorithms Radiography ab: Vertebral Compression Fracture (VCF) occurs when the vertebral body partially collapses under the action of compressive forces. Non-traumatic VCFs can be secondary to osteoporosis fragility (benign VCFs) or tumors (malignant VCFs). The investigation of the etiology of non-traumatic VCFs is usually necessary, since treatment and prognosis are dependent on the VCF type. Currently, there has been great interest in using Convolutional Neural Networks (CNNs) for the classification of medical images because these networks allow the automatic extraction of useful features for the classification in a given problem. However, CNNs usually require large datasets that are often not available in medical applications. Besides, these networks generally do not use additional information that may be important for classification. A different approach is to classify the image based on a large number of predefined features, an approach known as radiomics. In this work, we propose a hybrid method for classifying VCFs that uses features from three different sources: i) intermediate layers of CNNs; ii) radiomics; iii) additional clinical and image histogram information. In the hybrid method proposed here, external features are inserted as additional inputs to the first dense layer of a CNN. A Genetic Algorithm is used to: i) select a subset of radiomic, clinical, and histogram features relevant to the classification of VCFs; ii) select hyper-parameters of the CNN. Experiments using different models indicate that combining information is interesting to improve the performance of the classifier. Besides, pre-trained CNNs presents better performance than CNNs trained from scratch on the classification of VCFs. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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