Evaluation of Performance Metrics of Thyroid Segmentation by Deep Learning Technique

Q4 Biochemistry, Genetics and Molecular Biology International Journal of Biology and Biomedical Engineering Pub Date : 2021-07-22 DOI:10.46300/91011.2021.15.35
Nayana R. Shenoy, A. Jatti
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引用次数: 1

Abstract

Thyroid cancer is one of the commonly seen endocrine system cancer. Thyroid nodules appear as solid or fluid-filled masses on the thyroid. In many cases the thyroid nodules do not show any symptoms and due to this it leads to the critical situation up to death.All nodules are not cancerous and so it is very important to discriminate benign from malignant nodules. For diagnosing thyroid nodule the preferred imaging modality isUltrasound imaging.Due to inhomogeneous structure segmenting thyroid gland is a great challenge.Most of the researchers have implemented semi-automatic and automatic techniques to segment the nodules. In this paper we suggest a model to segment the region of interest by modifyingthe basic U-Netmodel. The performance metrics such as true positive, accuracy, F1-measure and dice coefficient is calculated and compared with basic model.
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利用深度学习技术评估甲状腺分割的性能指标
甲状腺癌症是常见的内分泌系统癌症之一。甲状腺结节表现为甲状腺上充满固体或液体的肿块。在许多情况下,甲状腺结节没有表现出任何症状,因此会导致危重情况直至死亡。所有的结节都不是癌性的,因此区分良性和恶性结节是非常重要的。诊断甲状腺结节的首选成像方式是超声成像。由于结构不均匀,分割甲状腺是一个巨大的挑战。大多数研究人员都采用了半自动和自动技术来分割结节。在本文中,我们提出了一个通过修改基本U-Net模型来分割感兴趣区域的模型。计算了真阳性、准确度、F1测度和骰子系数等性能指标,并与基本模型进行了比较。
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来源期刊
International Journal of Biology and Biomedical Engineering
International Journal of Biology and Biomedical Engineering Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
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42
期刊介绍: Topics: Molecular Dynamics, Biochemistry, Biophysics, Quantum Chemistry, Molecular Biology, Cell Biology, Immunology, Neurophysiology, Genetics, Population Dynamics, Dynamics of Diseases, Bioecology, Epidemiology, Social Dynamics, PhotoBiology, PhotoChemistry, Plant Biology, Microbiology, Immunology, Bioinformatics, Signal Transduction, Environmental Systems, Psychological and Cognitive Systems, Pattern Formation, Evolution, Game Theory and Adaptive Dynamics, Bioengineering, Biotechnolgies, Medical Imaging, Medical Signal Processing, Feedback Control in Biology and Chemistry, Fluid Mechanics and Applications in Biomedicine, Space Medicine and Biology, Nuclear Biology and Medicine.
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