Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs i...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 4; pp. 597 - 605 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137642032&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642032 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2019 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642032 137642032 137642032 10.1007/s10278-019-00220-4 137642032 ppf: 597 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks. aug: au: Stember, J. N. Celik, H. Krupinski, E. Chang, P. D. Mutasa, S. Wood, B. J. Lignelli, A. Moonis, G. Schwartz, L. H. Jambawalikar, S. Bagci, U. affil: Department of Radiology, Columbia University Medical Center - NYPH, 10032, New York, NY, USA sug: subj: Eye Movement Measurements Deep Learning Neural Networks (Computer) Utilization Image Interpretation, Computer Assisted Methods Human Artificial Intelligence Meningioma Algorithms Data Mining Methods Data Management Magnetic Resonance Imaging Diagnosis, Computer Assisted Image Enhancement ab: Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs is obtaining large-scale and precisely annotated imaging data. We sought to address the lack of annotated data with eye tracking technology. As a proof of principle, our hypothesis was that segmentation masks generated with the help of eye tracking (ET) would be very similar to those rendered by hand annotation (HA). Additionally, our goal was to show that a CNN trained on ET masks would be equivalent to one trained on HA masks, the latter being the current standard approach. Step 1: Screen captures of 19 publicly available radiologic images of assorted structures within various modalities were analyzed. ET and HA masks for all regions of interest (ROIs) were generated from these image datasets. Step 2: Utilizing a similar approach, ET and HA masks for 356 publicly available T1-weighted postcontrast meningioma images were generated. Three hundred six of these image + mask pairs were used to train a CNN with U-net-based architecture. The remaining 50 images were used as the independent test set. Step 1: ET and HA masks for the nonneurological images had an average Dice similarity coefficient (DSC) of 0.86 between each other. Step 2: Meningioma ET and HA masks had an average DSC of 0.85 between each other. After separate training using both approaches, the ET approach performed virtually identically to HA on the test set of 50 images. The former had an area under the curve (AUC) of 0.88, while the latter had AUC of 0.87. ET and HA predictions had trimmed mean DSCs compared to the original HA maps of 0.73 and 0.74, respectively. These trimmed DSCs between ET and HA were found to be statistically equivalent with a p value of 0.015. We have demonstrated that ET can create segmentation masks suitable for deep learning semantic segmentation. Future work will integrate ET to produce masks in a faster, more natural manner that distracts less from typical radiology clinical workflow. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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