Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Abstract

Objective:
There has been a considerable amount of Computer-Aided Diagnosis (CAD) methods highlighted in the field of ultrasonic examination (USE) of thyroid nodules. However, few researches focused on the automatic risk classification, which was the basis for determining whether Fine Needle Aspiration (FNA) was needed. The aim of this work was to implement automatic risk level assessment of thyroid nodules.
Approach:
Firstly, 1862 cases of thyroid nodules with the results of USE and FNA were collected as the dataset. Then, an improved U-Net++ model was utilized for segmenting thyroid nodules in ultrasound images automatically. Finally, the segmentation result was imported into a multi-task convolutional neural network (MT-CNN), the design of which was based on the clinical guideline called KWAK TI-RADS. Apart from the category of benign and malignant, the MT-CNN also exported the classification result of four malignant features, Solid Component (SC), Hypoechogenicity or Marked Hypoechogenicity (HMH), Microlobulated or Irregular Margin (MIM), Microcalcification (MC), which were used for counting the risk level in KWAK TI-RADS.
Main results:
The performance of the improved U-Net++ was evaluated on our test set, including 302 cases. The Dice coefficient and Intersection over Union (IoU) reached 0.899, 0.816, respectively. The classification accuracy rates of SC, HMH, MIM, MC, were 94.5%, 92.8%, 86.1%, 88.9%, while the false positive (FP) rate was 6.0%, 5.6%, 10.6%, 12.9% separately. As for the category of benign and malignant, the precision and recall rates were 93.7% and 94.4%.
Significance:
The proposed CAD method showed favourable performance in the diagnosis of thyroid nodules. Compared with other methods, it could provide reports closer to clinical practice for doctors.