A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor’s diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

Download Full-text

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

Journal of X-Ray Science and Technology ◽

10.3233/xst-210879 ◽

2021 ◽

pp. 1-19

Author(s):

Maria Tamoor ◽

Irfan Younas

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Medical Image ◽

Active Contour Model ◽

Heart Diseases ◽

Medical Image Segmentation ◽

Gaussian Kernel ◽

Initial Contour ◽

Automated Method ◽

Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

Download Full-text

Efficient Medical Image Segmentation Of COVID-19 Chest CT Images Based on Deep Learning Techniques

2021 International Conference on Emerging Smart Computing and Informatics (ESCI) ◽

10.1109/esci50559.2021.9397043 ◽

2021 ◽

Author(s):

Sanika Walvekar ◽

Swati Shinde

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Ct Images ◽

Medical Image Segmentation ◽

Chest Ct ◽

Learning Techniques

Download Full-text

Medical Image Segmentation Using Advanced Machine Learning Algorithms (Learning Active Contour Models)

SSRN Electronic Journal ◽

10.2139/ssrn.3794007 ◽

2021 ◽

Author(s):

Jubin Dipakkumar Kothari

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Active Contour ◽

Medical Image ◽

Learning Algorithms ◽

Medical Image Segmentation ◽

Machine Learning Algorithms ◽

Active Contour Models ◽

Contour Models

Download Full-text

HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽

10.3390/sym13112107 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2107

Author(s):

Xin Wei ◽

Huan Wan ◽

Fanghua Ye ◽

Weidong Min

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Loss Function ◽

Network Structure ◽

Medical Diagnosis ◽

Medical Image ◽

Medical Image Segmentation ◽

Training Mode ◽

Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

Download Full-text