Highly Efficient and Accurate Deep Learning–Based Classification of MRI Contrast on a CPU and GPU.
Classifying MR images based on their contrast mechanism can be useful in image segmentation where additional information from different contrast mechanisms can improve intensity-based segmentation and help separate the class distributions. In addition, automated processing of image type can be benef...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 482 - 496 |
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| Autor principal: | |
| Formato: | diagnostic images equations & formulas 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=157184676&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184676 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: 157184676 155159727 157184676 157184676 10.1007/s10278-022-00583-1 157184676 ppf: 482 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Highly Efficient and Accurate Deep Learning–Based Classification of MRI Contrast on a CPU and GPU. aug: au: Gai, Neville D. affil: Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA sug: subj: Deep Learning Magnetic Resonance Imaging Classification Contrast Media Image Processing, Computer Assisted Machine Learning Neural Networks (Computer) Brain Ultrasonography ab: Classifying MR images based on their contrast mechanism can be useful in image segmentation where additional information from different contrast mechanisms can improve intensity-based segmentation and help separate the class distributions. In addition, automated processing of image type can be beneficial in archive management, image retrieval, and staff training. Different clinics and scanners have their own image labeling scheme, resulting in ambiguity when sorting images. Manual sorting of thousands of images would be a laborious task and prone to error. In this work, we used the power of transfer learning to modify pretrained residual convolution neural networks to classify MRI images based on their contrast mechanisms. Training and validation were performed on a total of 5169 images belonging to 10 different classes and from different MRI vendors and field strengths. Time for training and validation was 36 min. Testing was performed on a different data set with 2474 images. Percentage of correctly classified images (accuracy) was 99.76%. (A deeper version of the residual network was trained for 103 min and showed slightly lower accuracy of 99.68%.) In consideration of model deployment in the real world, performance on a single CPU computer was compared with GPU implementation. Highly accurate classification, training, and testing can be achieved without use of a GPU in a relatively short training time, through proper choice of a convolutional neural network and hyperparameters, making it feasible to improve accuracy by repeated training with cumulative training sets. Techniques to improve accuracy further are discussed and demonstrated. Derived heatmaps indicate areas of image used in decision making and correspond well with expert human perception. The methods used can be easily extended to other classification tasks with minimal changes. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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