MARC details
| 000 -LEADER |
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| fixed length control field |
03640nam a22004337a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220124s20212021 xxu||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
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| International Standard Serial Number |
1361-8415 |
| 024 ## - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1016/j.media.2021.102262 [doi] |
| 024 ## - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
S1361-8415(21)00307-8 [pii] |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
Ovid MEDLINE(R) |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
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| PMID |
34670148 |
| 245 ## - TITLE STATEMENT |
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| Title |
Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. |
| 251 ## - Source |
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| Source |
Medical Image Analysis. 75:102262, 2022 01. |
| 252 ## - Abbreviated Source |
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| Abbreviated source |
Med Image Anal. 75:102262, 2022 01. |
| 252 ## - Abbreviated Source |
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| Former abbreviated source |
Med Image Anal. 75:102262, 2022 Jan. |
| 253 ## - Journal Name |
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| Journal name |
Medical image analysis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Year |
2022 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Manufacturer |
FY2022 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Publication date |
2022 Jan |
| 265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE] |
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| Publication status |
ppublish |
| 266 ## - Date added to catalog |
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| Date added to catalog |
2022-01-25 |
| 268 ## - Previous citation |
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| -- |
Medical Image Analysis. 75:102262, 2022 Jan. |
| 269 ## - Original dates |
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| Original fiscal year |
FY2022 |
| 520 ## - SUMMARY, ETC. |
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| Abstract |
Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets. Copyright (c) 2021. Published by Elsevier B.V. |
| 546 ## - LANGUAGE NOTE |
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| Language note |
English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
*Coronary Vessels |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
*Ultrasonography, Interventional |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Algorithms |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Coronary Vessels/dg [Diagnostic Imaging] |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Humans |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Ultrasonography |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
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| Institution |
MedStar Heart & Vascular Institute |
| 657 ## - INDEX TERM--FUNCTION |
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| Medline publication type |
Journal Article |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Local Authors |
Bass, Ronald |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Local Authors |
Garcia-Garcia, Hector M |
| 790 ## - Authors |
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| All authors |
Bass R, Blanco PJ, Bulant CA, Garcia-Garcia HM, Lemos PA, Raber L, Ueki Y, Ziemer PGP |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| DOI |
<a href="https://dx.doi.org/10.1016/j.media.2021.102262">https://dx.doi.org/10.1016/j.media.2021.102262</a> |
| Public note |
https://dx.doi.org/10.1016/j.media.2021.102262 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Journal Article |
| Item type description |
Article |
