A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning

• Autorzy: Ilesanmi Ademola Enitan , Chaumrattanakul Utairat , Makhanov Stanislav S.

Identyfikatory

DOI

10.1016/j.bbe.2021.05.007

• Języki publikacji: EN

Abstrakty

EN

Background: Breast cancer is a deadly disease responsible for statistical yearly global death. Identification of cancer tumors is quite tasking, as a result, concerted efforts are thus devoted. Clinicians have used ultrasounds as a diagnostic tool for breast cancer, though, poor image quality is a major limitation when segmenting breast ultrasound. To address this problem, we present a semantic segmentation method for breast ultrasound (BUS) images. Method: The BUS images were resized and then enhanced with the contrast limited adaptive histogram equalization method. Subsequently, the variant enhanced block was used to encode the preprocessed image. Finally, the concatenated convolutions produced the segmentation mask. Results: The proposed method was evaluated with two datasets. The datasets contain 264 and 830 BUS images respectively. Dice measure (DM), Jaccard measure, and Hausdroff distance were used to evaluate the methods. Results indicate that the proposed method achieves high DM with 89.73% for malignant and 89.62% for benign BUSs. Moreover, the results obtained validate the capacity of the proposed method to achieve higher DM in comparison with reported methods. Conclusion: The proposed algorithm provides a deep learning segmentation procedure that can segment tumors in BUS images effectively and efficiently.

Słowa kluczowe

EN

breast ultrasound image; speckle noise; deep learning; semantic segmentation; tumor segmentation

PL

ultrasonografia piersi; szum plamkowy; uczenie głębokie; segmentacja semantyczna; segmentacja guza

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 802--818
• Opis fizyczny: Bibliogr. 60 poz., rys., tab., wykr.

Twórcy

autor

Ilesanmi Ademola Enitan

• School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Thailand

autor

Chaumrattanakul Utairat

• Department of Radiology, Thammasat University, Pathum Thani, Thailand

autor

Makhanov Stanislav S.

• School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Thailand

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).