# Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

arXiv: https://arxiv.org/abs/2310.01861 · HTML: https://mlsanity.com/paper/2310.01861/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement](https://mlsanity.com/paper/2010.07958/) (2020)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021)
- [Adaptive Context Selection for Polyp Segmentation](https://mlsanity.com/paper/2301.04799/) (2023)
- [A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos](https://mlsanity.com/paper/2207.00141/) (2022)

## Built on by (most cited first, 16 of 16)

- [Vivim: a Video Vision Mamba for Medical Video Segmentation](https://mlsanity.com/paper/2401.14168/) (2024)
- [SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation](https://mlsanity.com/paper/2406.13532/) (2024)
- [Biomedical SAM 2: Segment Anything in Biomedical Images and Videos](https://mlsanity.com/paper/2408.03286/) (2024)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model](https://mlsanity.com/paper/2409.09484/) (2024)
- [Text-Promptable Propagation for Referring Medical Image Sequence Segmentation](https://mlsanity.com/paper/2502.11093/) (2025)
- [HilbertMamba: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos](https://mlsanity.com/paper/2407.05703/) (2024)
- [Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development](https://mlsanity.com/paper/2408.10067/) (2024)
- [PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM](https://mlsanity.com/paper/2402.11585/) (2024)
- [A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos](https://mlsanity.com/paper/2403.11699/) (2024)
- [Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound](https://mlsanity.com/paper/2402.04921/) (2024)
- [One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks](https://mlsanity.com/paper/2503.14979/) (2025)
- [Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation](https://mlsanity.com/paper/2507.07443/) (2025)
- [Tracking spatial temporal details in ultrasound long video via wavelet analysis and memory bank](https://mlsanity.com/paper/2512.15066/) (2025)
- [Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation](https://mlsanity.com/paper/2606.31198/) (2026)
- [OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations](https://mlsanity.com/paper/2607.06007/) (2026)

## Abstract (arXiv)

Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-quality annotations has posed a persistent challenge for the research community. To overcome this issue, we meticulously curated a US video breast lesion segmentation dataset comprising 572 videos and 34,300 annotated frames, covering a wide range of realistic clinical scenarios. Furthermore, we propose a novel frequency and localization feature aggregation network (FLA-Net) that learns temporal features from the frequency domain and predicts additional lesion location positions to assist with breast lesion segmentation. We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos. Our experiments on our annotated dataset and two public video polyp segmentation datasets demonstrate that our proposed FLA-Net achieves state-of-the-art performance in breast lesion segmentation in US videos and video polyp segmentation while significantly reducing time and space complexity. Our model and dataset are available at https://github.com/jhl-Det/FLA-Net.