Dual-Modality Watershed Fusion Network for Thyroid Nodule Classification of Dual-View CEUS Video

Contrast-enhanced ultrasound (CEUS) allows real-time visualization of the vascular distribution within thyroid nodules, garnering significant attention in their intelligent diagnosis. Existing methods either focus on modifying models while neglecting the unique aspects of CEUS, or rely only single-modality data while overlooking the complementary information contained in the dual-view CEUS data. To overcome these limitations, inspired by the CEUS thyroid imaging reporting and data system (TI-RADS), this paper proposes a new dual-modality watershed fusion network (DWFN) for diagnosing thyroid nodules using dual-view CEUS videos. Specifically, the method introduces the watershed analysis from the remote sensing field and combines it with the optical flow method to extract the enhancement direction feature mentioned in the CEUS TI-RADS. On this basis, the interpretable watershed 3D network (W3DN) is constructed by C3D to further extract the dynamic blood flow features contained in CEUS videos. Furthermore, to make more comprehensive use of clinical information, a dual-modality 2D and 3D combined network, DWFN is constructed, which fuses the morphological features extracted from US images by InceptionResNetV2 and the dynamic blood flow features extracted from CEUS videos by W3DN, to classify thyroid nodules as benign or malignant. The effectiveness of the proposed DWFN method was evaluated using extensive experimental results on a collected dataset of dual-view CEUS videos for thyroid nodules, achieving an area under the receiver operating characteristic curve of 0.920, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score of 0.858, 0.845, 0.872, 0.879, 0.837, and 0.861, respectively, outperforming other state-of-the-art methods.