Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach.
Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1016 - 1029 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
|
| 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=164473083&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473083 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473083 162043671 164473083 164473083 10.1007/s10278-022-00751-3 164473083 ppf: 1016 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach. aug: au: Marasinou, Chrysostomos Li, Bo Paige, Jeremy Omigbodun, Akinyinka Nakhaei, Noor Hoyt, Anne Hsu, William affil: Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd, Ste 420, 90024, Los Angeles, USA sug: subj: Breast Diseases Diagnosis Radiographic Image Enhancement Methods Mammography Sensitivity and Specificity Evaluation Human Quantitative Studies Carcinoma, Ductal Diagnosis Regression Funding Source Diagnostic Errors Prevention and Control Breast Neoplasms Diagnosis Neoplasm Invasiveness ab: Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|