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Objective assessment of segmentation models for thyroid ultrasound images

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Abstract

Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ultrasonography B-mode scans, a multi-output CNN-based semantic segmentation is used to separate thyroid nodules' cystic & solid components. Semantic segmentation is an automatic technique that labels the ultrasound (US) pixels with an appropriate class or pixel category, i.e., belongs to a lesion or background. In the present study, encoder-decoder-based semantic segmentation models i.e. SegNet using VGG16, UNet, and Hybrid-UNet implemented for segmentation of thyroid US images. For this work, 820 thyroid US images are collected from the DDTI and ultrasoundcases.info (USC) datasets. These segmentation models were trained using a transfer learning approach with original and despeckled thyroid US images. The performance of segmentation models is evaluated by analyzing the overlap region between the true contour lesion marked by the radiologist and the lesion retrieved by the segmentation model. The mean intersection of union (mIoU), mean dice coefficient (mDC) metrics, TPR, TNR, FPR, and FNR metrics are used to measure performance. Based on the exhaustive experiments and performance evaluation parameters it is observed that the proposed Hybrid-UNet segmentation model segments thyroid nodules and cystic components effectively.

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Acknowledgements

The authors would like to thanks Dr. Jyotsna Sen, Sr. Professor, department of radiodiagnosis, Pt. B. D. Sharma Postgraduate Institute of Medical Sciences, Rohtak for stimulating discussions regarding different sonographic characteristics exhibited by various types of benign and malignant thyroid tumors. The first author acknowledge “National Project Implementation Unit (NPIU), a unit of Ministry of Human Resource Development, Government of India” for the financial assistantship through TEQIP-III project as Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India.

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This study and authors not received any funding from other sources.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India

    Niranjan Yadav & Rajeshwar Dass

  2. Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India

    Jitendra Virmani

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Yadav, N., Dass, R. & Virmani, J. Objective assessment of segmentation models for thyroid ultrasound images. J Ultrasound 26, 673–685 (2023). https://doi.org/10.1007/s40477-022-00726-8

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