  Breast tumor segmentation is one of the key steps that helps us characterize
and localize tumor regions. However, variable tumor morphology, blurred
boundary, and similar intensity distributions bring challenges for accurate
segmentation of breast tumors. Recently, many U-net variants have been proposed
and widely used for breast tumors segmentation. However, these architectures
suffer from two limitations: (1) Ignoring the characterize ability of the
benchmark networks, and (2) Introducing extra complex operations increases the
difficulty of understanding and reproducing the network. To alleviate these
challenges, this paper proposes a simple yet powerful nested U-net (NU-net) for
accurate segmentation of breast tumors. The key idea is to utilize U-Nets with
different depths and shared weights to achieve robust characterization of
breast tumors. NU-net mainly has the following advantages: (1) Improving
network adaptability and robustness to breast tumors with different scales, (2)
This method is easy to reproduce and execute, and (3) The extra operations
increase network parameters without significantly increasing computational
cost. Extensive experimental results with twelve state-of-the-art segmentation
methods on three public breast ultrasound datasets demonstrate that NU-net has
more competitive segmentation performance on breast tumors. Furthermore, the
robustness of NU-net is further illustrated on the segmentation of renal
ultrasound images. The source code is publicly available on
https://github.com/CGPzy/NU-net.
