# Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation

arXiv: https://arxiv.org/abs/2209.07193 · HTML: https://mlsanity.com/paper/2209.07193/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.12077/) (2022)
- [BUSIS: A Benchmark for Breast Ultrasound Image Segmentation](https://mlsanity.com/paper/1801.03182/) (2018)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
- [Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images](https://mlsanity.com/paper/1710.03778/) (2017)
- [Automatic Breast Ultrasound Image Segmentation: A Survey](https://mlsanity.com/paper/1704.01472/) (2017)
- [RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging](https://mlsanity.com/paper/2108.02508/) (2021)
- [Stan: Small tumor-aware network for breast ultrasound image segmentation](https://mlsanity.com/paper/2002.01034/) (2020)
- [BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation](https://mlsanity.com/paper/2204.13342/) (2022)

## Built on by (most cited first, 13 of 13)

- [ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation](https://mlsanity.com/paper/2211.02915/) (2022)
- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Transforming Surgical Interventions with Embodied Intelligence for Ultrasound Robotics](https://mlsanity.com/paper/2406.12651/) (2024)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance](https://mlsanity.com/paper/2408.15026/) (2024)
- [Pubic Symphysis-Fetal Head Segmentation Network Using BiFormer Attention Mechanism and Multipath Dilated Convolution](https://mlsanity.com/paper/2410.10352/) (2024)
- [ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation](https://mlsanity.com/paper/2302.09765/) (2023)
- [BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks](https://mlsanity.com/paper/2310.00919/) (2023)
- [M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans](https://mlsanity.com/paper/2401.10419/) (2024)
- [DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation](https://mlsanity.com/paper/2411.11116/) (2024)
- [UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction](https://mlsanity.com/paper/2502.03783/) (2025)
- [DFEN: Dual Feature Equalization Network for Medical Image Segmentation](https://mlsanity.com/paper/2505.05913/) (2025)
- [Fracture interactive geodesic active contours for bone segmentation](https://mlsanity.com/paper/2509.14817/) (2025)

## Abstract (arXiv)

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.