Search
Abstract
Background: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using arti- ficial intelligence. However, only two papers have dealt with complex BI-RADS classifications. Purpose: This study addresses the automatic classification of breast lesions into binary classes (benign vs. ma- lignant) and multiple BI-RADS classes based on a single ultrasonographic image. Achieving this task should reduce the subjectivity of an individual operator’s assessment. Materials and Methods: Automatic image segmentation methods (PraNet, CaraNet and FCBFormer) adapted to the specific segmentation task were investigated using the U-Net model as a reference. A new classification method was developed using an ensemble of selected segmentation approaches. All experiments were performed on publicly available BUS B, OASBUD, BUSI and private datasets. Results: FCBFormer achieved the best outcomes for the segmentation task with intersection over union metric values of 0.81, 0.80 and 0.73 and Dice values of 0.89, 0.87 and 0.82, respectively, for the BUS B, BUSI and OASBUD datasets. Through a series of experiments, we determined that adding an extra 30-pixel margin to the segmentation mask counteracts the potential errors introduced by the segmentation algorithm. An assembly of the full image classifier, bounding box classifier and masked image classifier was the most accurate for binary classification and had the best accuracy (ACC; 0.908), F1 (0.846) and area under the receiver operating char- acteristics curve (AUROC; 0.871) in the BUS B and ACC (0.982), F1 (0.984) and AUROC (0.998) in the UCC BUS datasets, outperforming each classifier used separately. It was also the most effective for BI-RADS classification, with ACC of 0.953, F1 of 0.920 and AUROC of 0.986 in UCC BUS. Hard voting was the most effective method for dichotomous classification. For the multi-class BI-RADS classification, the soft voting approach was employed. Conclusions: The proposed new classification approach with an ensemble of segmentation and classification approaches proved more accurate than most published results for binary and multi-class BI-RADS classifications.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (7)
-
Mikołaj Badocha
-
Katarzyna Gwozdziewicz
-
Marlena Rygusik
-
Paulina Kalinowska
-
Edyta Szurowska prof. dr hab.
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ijmedinf.2024.105522
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
no. 189,
ISSN: 1386-5056 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Bobowicz M., Badocha M., Gwozdziewicz K., Rygusik M., Kalinowska P., Szurowska E., Dziubich T.: Segmentation-Based BI-RADS ensemble classification of breast tumours in ultrasound images// INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS -, (2024), s.105522-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ijmedinf.2024.105522
- Sources of funding:
-
- Koszt publikacji poniósł w całości Gdański Uniwersytet Medyczny korzystając z opcji publikacji: Gold Open Access
- Verified by:
- Gdańsk University of Technology
seen 62 times
Recommended for you
Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
- N. Sohail,
- S. M. Anwar,
- F. Majeed
- + 1 authors