SK-Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images.

Abstract

The quality of segmentation of thyroid nodules in ultrasound images is a crucial factor in preventing the cancerization of thyroid nodules. However, the existing standards for the ultrasound imaging of cancerous nodules have limitations, and changes of the echo pattern of thyroid nodules pose challenges in accurately segmenting nodules, which can affect the diagnostic results of medical professionals. The aim of this study is to address the challenges related to segmentation accuracy due to noise, low contrast, morphological scale variations, and blurred edges of thyroid nodules in ultrasound images and improve the accuracy of ultrasound-based thyroid nodule segmentation, thereby aiding the clinical diagnosis of thyroid nodules. In this study, the dataset of thyroid ultrasound images was obtained from Hunan Provincial People's Hospital, consisting of a total of 3572 samples used for the training, validation, and testing of this model at a ratio of 8:1:1. A novel SK-Unet++ network was used to enhance the segmentation accuracy of thyroid nodules. SK-Unet++ is a novel deep learning architecture that adds the adaptive receptive fields based on the selective kernel (SK) attention mechanisms into the Unet++ network. The convolution blocks of the original UNet++ encoder part were replaced with finer SK convolution blocks in SK-Unet++. First, multiple skip connections were incorporated so that SK-Unet++ can make information from previous layers of the neural network to bypass certain layers and directly propagate to subsequent layers. The feature maps of the corresponding locations were fused on the channel, resulting in enhanced segmentation accuracy. Second, we added the adaptive receptive fields. The adaptive receptive fields were used to capture multiscale spatial features better by dynamically adjusting its receptive field. The assessment metrics contained dice similarity coefficient (Dsc), accuracy (Acc), precision (Pre), recall (Re), and Hausdorff distance, and all comparison experiments used the paired t-tests to assess whether statistically significant performance differences existed (p<0.05). And to address the multi-comparison problem, we performed the false discovery rate (FDR) correction after the test. The segmentation model had an Acc of 80.6%, Dsc of 84.7%, Pre of 77.5%, Re of 71.7%, and an average Hausdorff distance of 15.80mm. Ablation experimental results demonstrated that each module in the network could contribute to the improved performance (p<0.05) and determined the best combination of parameters. A comparison with other state-of-the-art methods showed that SK-Unet++ significantly outperformed them in terms of segmentation performance (p<0.05), with a more accurate segmentation contour. Additionally, the adaptive weight changes of the SK module were monitored during the training process, and the resulting change curves demonstrated their convergence. Our proposed method demonstrates favorable performance in the segmentation of ultrasound images of thyroid nodules. Results confirmed that SK-Unet++ is a feasible and effective method for the automatic segmentation of thyroid nodules in ultrasound images. The high accuracy achieved by our method can facilitate efficient screening of patients with thyroid nodules, ultimately reducing the workload of clinicians and radiologists.