Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow.
Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time....
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 3; pp. 541 - 554 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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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=151702169&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151702169 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2021 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151702169 150451426 151702169 151702169 10.1007/s10278-021-00460-3 151702169 ppf: 541 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. aug: au: Kiser, Kendall J. Barman, Arko Stieb, Sonja Fuller, Clifton D. Giancardo, Luca affil: Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA sug: subj: Radiography, Thoracic Workflow Automation Image Processing, Computer Assisted Methods Human Neural Networks (Computer) Spearman's Rank Correlation Coefficient Mann-Whitney U Test Descriptive Statistics Machine Learning Time Tomography, X-Ray Computed ab: Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman's rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman's rank correlation coefficients or Mann–Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = − 0.48 versus ρ = − 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool's training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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