# MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

arXiv: https://arxiv.org/abs/2305.19956 · HTML: https://mlsanity.com/paper/2305.19956/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation](https://mlsanity.com/paper/1902.08128/) (2019)
- [CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI](https://mlsanity.com/paper/2203.15163/) (2022)
- [Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images](https://mlsanity.com/paper/1612.01655/) (2016)

## Built on by (most cited first, 25 of 25)

- [UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasets](https://mlsanity.com/paper/2411.16222/) (2024)
- [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024)
- [A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention](https://mlsanity.com/paper/2405.00130/) (2024)
- [Improving Representation of High-frequency Components for Medical Visual Foundation Models](https://mlsanity.com/paper/2407.14651/) (2024)
- [MedicoSAM: Robust Improvement of SAM for Medical Imaging](https://mlsanity.com/paper/2501.11734/) (2025)
- [Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis](https://mlsanity.com/paper/2506.08849/) (2025)
- [CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention](https://mlsanity.com/paper/2401.13049/) (2024)
- [ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection](https://mlsanity.com/paper/2510.26703/) (2025)
- [Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops](https://mlsanity.com/paper/2501.12331/) (2025)
- [Text-Promptable Propagation for Referring Medical Image Sequence Segmentation](https://mlsanity.com/paper/2502.11093/) (2025)
- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models](https://mlsanity.com/paper/2506.23903/) (2025)
- [SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI](https://mlsanity.com/paper/2401.09627/) (2024)
- [Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound](https://mlsanity.com/paper/2412.10997/) (2024)
- [Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2603.18655/) (2026)
- [CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation](https://mlsanity.com/paper/2406.14186/) (2024)
- [Prostate Cancer Screening with Artificial Intelligence-Enhanced Micro-Ultrasound: A Comparative Study with Traditional Methods](https://mlsanity.com/paper/2505.21355/) (2025)
- [Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation](https://mlsanity.com/paper/2507.07443/) (2025)
- [Prostate Capsule Segmentation from Micro-Ultrasound Images using Adaptive Focal Loss](https://mlsanity.com/paper/2509.15595/) (2025)
- [Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection](https://mlsanity.com/paper/2606.30951/) (2026)
- [Compass: Prostate Cancer Detection Needs Multi-View Context](https://mlsanity.com/paper/2607.06919/) (2026)
- [Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation](https://mlsanity.com/paper/2607.21787/) (2026)
- [Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation](https://mlsanity.com/paper/2608.14796/) (2026)
- [Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound](https://mlsanity.com/paper/2608.20557/) (2026)
- [UltraPIPS: Improving model perception in B-mode ultrasound with foundation models](https://mlsanity.com/paper/2608.26033/) (2026)

## Abstract (arXiv)

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly available at https://zenodo.org/records/10475293.