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

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.