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CIL-Net: Densely Connected Context Information Learning Network for Boosting Thyroid Nodule Segmentation Using Ultrasound Images

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Abstract

Thyroid nodule (TYN) is a life-threatening disease that is commonly observed among adults globally. The applications of deep learning in computer-aided diagnosis systems (CADs) for diagnosing thyroid nodules have attracted attention among clinical professionals due to their significantly potential role in reducing the occurrence of missed diagnoses. However, most techniques for segmenting thyroid nodules rely on U-Net structures or deep convolutional neural networks, which have limitations in obtaining different context information due to the diversities in the shapes and sizes, ambiguous boundaries, and heterostructure of thyroid nodules. To resolve these challenges, we present an encoder-decoder-based architecture (referred to as CIL-Net) for boosting TYN segmentation. There are three contributions in the proposed CIL-Net. First, the encoder is established using dense connectivity for efficient feature extraction and the triplet attention block (TAB) for highlighting essential feature maps. Second, we design a feature improvement block (FIB) using dilated convolutions and attention mechanisms to capture the global context information and also build up robust feature maps between the encoder-decoder branches. Third, we introduce the residual context block (RCB), which leverages residual units (ResUnits) to accumulate the context information from the multiple blocks of decoders in the decoder branch. We assess the segmentation quality of our proposed method using six different evaluation metrics on two standard datasets (DDTI and TN3K) of TYN and demonstrate competitive performance against advanced state-of-the-art methods. We consider that the proposed method advances the performance of TYN region localization and segmentation, which heavily rely on an accurate assessment of different context information. This advancement is primarily attributed to the comprehensive incorporation of dense connectivity, TAB, FIB, and RCB, which effectively capture both extensive and intricate contextual details. We anticipate that this approach reliability and visual explainability make it a valuable tool that holds the potential to significantly enhance clinical practices by offering reliable predictions to facilitate cognitive and healthcare decision-making.

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Data Availability

In this study, publicly accessible datasets have been examined. This data can be found here: https://drive.google.com/file/d/1reHyY5eTZ5uePXMVMzFOq5j3eFOSp50F/view and https://drive.google.com/file/d/1wwlsEhwfSyvQsJBRjeDLhUjqZh8eaH2R/view.

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Funding

This work is supported in part by the National Natural Science Foundation of China under Grant No. of 62076159, 12031010, 61673251 and is also supported by the Fundamental Research Funds for the Central Universities under Grant No. of GK202105003, GK202304044.

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  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, Shaanxi, China

    Haider Ali, Mingzhao Wang & Juanying Xie

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  1. Haider Ali
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Contributions

Haider Ali: conceptualization, methodology, writing—original draft, writing—review and editing, visualization. Mingzhao Wang: investigation. Juanying Xie: supervision, investigation, writing—review and editing.

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Correspondence to Juanying Xie.

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Ali, H., Wang, M. & Xie, J. CIL-Net: Densely Connected Context Information Learning Network for Boosting Thyroid Nodule Segmentation Using Ultrasound Images. Cogn Comput 16, 1176–1197 (2024). https://doi.org/10.1007/s12559-024-10289-x

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