# A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound

arXiv: https://arxiv.org/abs/2407.00678 · HTML: https://mlsanity.com/paper/2407.00678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 273 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 29 of 29)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [Label-driven weakly-supervised learning for multimodal deformable image registration](https://mlsanity.com/paper/1711.01666/) (2017)
- [MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images](https://mlsanity.com/paper/2305.19956/) (2023)
- [Learning Deep Similarity Metric for 3D MR-TRUS Registration](https://mlsanity.com/paper/1806.04548/) (2018)
- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Cross-modal Attention for MRI and Ultrasound Volume Registration](https://mlsanity.com/paper/2107.04548/) (2021)
- [Adversarial Image Registration with Application for MR and TRUS Image Fusion](https://mlsanity.com/paper/1804.11024/) (2018)
- [End-to-end Ultrasound Frame to Volume Registration](https://mlsanity.com/paper/2107.06449/) (2021)
- [Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images](https://mlsanity.com/paper/1612.01655/) (2016)
- [Adversarial Deformation Regularization for Training Image Registration Neural Networks](https://mlsanity.com/paper/1805.10665/) (2018)
- [Real-time multimodal image registration with partial intraoperative point-set data](https://mlsanity.com/paper/2109.05023/) (2021)
- [Multimodality Biomedical Image Registration using Free Point Transformer Networks](https://mlsanity.com/paper/2008.01885/) (2020)
- [Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound](https://mlsanity.com/paper/2207.10485/) (2022)
- [Non-rigid Medical Image Registration using Physics-informed Neural Networks](https://mlsanity.com/paper/2302.10343/) (2023)
- [TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision](https://mlsanity.com/paper/2303.02128/) (2023)
- [Conditional Segmentation in Lieu of Image Registration](https://mlsanity.com/paper/1907.00438/) (2019)
- [Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound](https://mlsanity.com/paper/2211.00527/) (2022)
- [Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence](https://mlsanity.com/paper/2102.04938/) (2021)
- [Meta-Learning Initializations for Interactive Medical Image Registration](https://mlsanity.com/paper/2210.15371/) (2022)
- [Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images](https://mlsanity.com/paper/2308.11376/) (2023)
- [Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound](https://mlsanity.com/paper/2406.14069/) (2024)
- [Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancer](https://mlsanity.com/paper/2402.08987/) (2024)
- [Distance Map Supervised Landmark Localization for MR-TRUS Registration](https://mlsanity.com/paper/2210.05738/) (2022)

## Built on by (most cited first, 3 of 3)

- [MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging](https://mlsanity.com/paper/2509.16806/) (2025)
- [Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound](https://mlsanity.com/paper/2507.03421/) (2025)
- [Salient Region Matching for Fully Automated MR-TRUS Registration](https://mlsanity.com/paper/2501.03510/) (2025)

## Abstract (arXiv)

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention of PCa.To facilitate physicians with more accurate and efficient computer-assisted diagnosis and interventions, many image processing algorithms in TRUS have been proposed and achieved state-of-the-art performance in several tasks, including prostate gland segmentation, prostate image registration, PCa classification and detection, and interventional needle detection. The rapid development of these algorithms over the past two decades necessitates a comprehensive summary. In consequence, this survey provides a textcolorbluenarrative  analysis of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions. Furthermore, this survey discusses current challenges and suggests future research directions to possibly advance this field further.