Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

Wang, Mingyu; Yuan, Chenglang; Wu, Dasheng; Zeng, Yinghou; Zhong, Shaonan; Qiu, Weibao

doi:10.1007/978-3-030-71827-5_14

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…The quantitative comparison in the DDTI dataset is shown in Table 2. It can be noted that our N-Net has a gap compared with the first place in the TN-SCUI2020 network (Wang et al, 2020). Due to a large number of model parameters, the average time to predict each ultrasound image will increase, which is unfavored by clinicians in diagnosis in real time.…”

Section: Comparisons With DI Erent Segmentation Methodsmentioning

confidence: 99%

“…In recent years, models based on DCNN have demonstrated significant improvements IN thyroid nodule segmentation (Ma et al, 2017 ; Ying et al, 2018 ; Shen et al, 2020 ; Tang and Ma, 2020 ; Wang et al, 2020 ; Zhang et al, 2020 ). In Ying et al ( 2018 ) proposed a method using cascaded U-Net and VGG-19 (Simonyan and Zisserman, 2014 ) network to segment the ROI area of thyroid nodules to assist doctors in diagnosis.…”

Section: Related Studymentioning

confidence: 99%

“…As the first place in the segmentation task ranking in the TN-SCUI2020 challenge. Wang et al ( 2020 ) proposed a cascaded segmentation framework and a resnet-based dual attention classification network to improve the segmentation and classification of thyroid nodules, respectively. In the future study, in order to be applied to assist doctors in the formulation of preoperative plans, the network can be designed to be lighter and faster while retaining most of the accuracy of the algorithm.…”

Section: Related Studymentioning

confidence: 99%

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N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

et al. 2022

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Medical image segmentation is an essential component of computer-aided diagnosis (CAD) systems. Thyroid nodule segmentation using ultrasound images is a necessary step for the early diagnosis of thyroid diseases. An encoder-decoder based deep convolutional neural network (DCNN), like U-Net architecture and its variants, has been extensively used to deal with medical image segmentation tasks. In this article, we propose a novel N-shape dense fully convolutional neural network for medical image segmentation, referred to as N-Net. The proposed framework is composed of three major components: a multi-scale input layer, an attention guidance module, and an innovative stackable dilated convolution (SDC) block. First, we apply the multi-scale input layer to construct an image pyramid, which achieves multi-level receiver field sizes and obtains rich feature representation. After that, the U-shape convolutional network is employed as the backbone structure. Moreover, we use the attention guidance module to filter the features before several skip connections, which can transfer structural information from previous feature maps to the following layers. This module can also remove noise and reduce the negative impact of the background. Finally, we propose a stackable dilated convolution (SDC) block, which is able to capture deep semantic features that may be lost in bilinear upsampling. We have evaluated the proposed N-Net framework on a thyroid nodule ultrasound image dataset (called the TNUI-2021 dataset) and the DDTI publicly available dataset. The experimental results show that our N-Net model outperforms several state-of-the-art methods in the thyroid nodule segmentation tasks.

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Section: Comparisons With DI Erent Segmentation Methodsmentioning

confidence: 99%

Section: Related Studymentioning

confidence: 99%

Section: Related Studymentioning

confidence: 99%

See 1 more Smart Citation

N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

et al. 2022

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“…Zhang et al ( 2019a ) adopted a tripartite classification module based on CNN model to pick out nodules information in ultrasound images. Wang et al ( 2020 ) proposed a dual-attention ResNet-based classification network to automatically achieve the accurate classification of thyroid nodules. Specifically, they adopted ResNet200 as the backbone network architecture to perform the classification of thyroid nodules while there is the problem that the classification network used for natural images does not fully adapt to medical images.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification

et al. 2022

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Automated thyroid nodule classification in ultrasound images is an important way to detect thyroid nodules and to make a more accurate diagnosis. In this paper, we propose a novel deep convolutional neural network (CNN) model, called n-ClsNet, for thyroid nodule classification. Our model consists of a multi-scale classification layer, multiple skip blocks, and a hybrid atrous convolution (HAC) block. The multi-scale classification layer first obtains multi-scale feature maps in order to make full use of image features. After that, each skip-block propagates information at different scales to learn multi-scale features for image classification. Finally, the HAC block is used to replace the downpooling layer so that the spatial information can be fully learned. We have evaluated our n-ClsNet model on the TNUI-2021 dataset. The proposed n-ClsNet achieves an average accuracy (ACC) score of 93.8% in the thyroid nodule classification task, which outperforms several representative state-of-the-art classification methods.

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MTN-Net: A Multi-Task Network for Detection and Segmentation of Thyroid Nodules in Ultrasound Images

Chen

Zheng²,

Hu³

2022

Knowledge Science, Engineering and Management

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Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

Cited by 7 publications

References 4 publications

N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation

A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification

MTN-Net: A Multi-Task Network for Detection and Segmentation of Thyroid Nodules in Ultrasound Images

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