Automatic segmentation of ultrasound images using SegNet and local Nakagami distribution fitting model. (March 2023) - Cui, Wenchao; Meng, Dan; Lu, Ke; Wu, Yirong; Pan, Zhihong; Li, Xiaolong; Sun, Shuifa
Highlights: Propose a coarse-to-fine segmentation method combining deep learning with an active contour model (ACM). Describe ultrasound images using local Nakagami distributions. A novel ACM was proposed based on the local Nakagami distribution fitting (LNDF) energy. A SegNet was employed to implement the coarse segmentation, which would be refined by the LNDF ACM. Adapt to automatic segmentation of US images in clinical applications. Abstract: Automatic and accurate segmentation of ultrasound (US) images plays a vital role in computer-aided diagnosis of many diseases. However, multiple degradations within US images, such as speckle noise, intensity inhomogeneity, shadows, low contrast, and low signal-to-noise ratio, often cover up the details of imaging tissues and blur the edges of lesions, resulting in limited accuracy for existing segmentation algorithms. To tackle these issues, a coarse-to-fine segmentation method combining deep learning with an active contour model (ACM) is proposed in this paper. At the first stage, a SegNet is trained and employed to predict the segmentation mask because it not only has a superior boundary delineation capability, but also has high efficiency in terms of memory and computational time during inference. Due to the multiple degradations of US images, the unavailability of large-scale US image datasets, and the loss of spatial information during downsampling, the SegNet may only infer the coarse object boundaries, especially for the tissue with blurred edges. At the second stage, the segmentation mask of the SegNet is refined to locate the accurate object boundaries by a novel ACM termed the local Nakagami distribution fitting (LNDF) model, which takes advantage of discrepancies between Nakagami distributions of different tissues around the boundaries within US images. Experimental results on two US image datasets demonstrate that, compared with some state-of-the-art segmentation methods, our proposed method achieves the highest segmentation accuracy at the cost of acceptable running time, and thereby adapts to the automatic segmentation of US images in clinical applications.