# Deeply-Supervised CNN for Prostate Segmentation

arXiv: https://arxiv.org/abs/1703.07523 · HTML: https://mlsanity.com/paper/1703.07523/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 30 of 30)

- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation](https://mlsanity.com/paper/2212.04497/) (2022)
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- [SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.10156/) (2024)
- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation](https://mlsanity.com/paper/2301.02393/) (2023)
- [A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.14134/) (2022)
- [Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT](https://mlsanity.com/paper/2208.06313/) (2022)
- [Radio astronomical images object detection and segmentation: A benchmark on deep learning methods](https://mlsanity.com/paper/2303.04506/) (2023)
- [Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework](https://mlsanity.com/paper/2304.00749/) (2023)
- [TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs](https://mlsanity.com/paper/2411.03464/) (2024)
- [ADS_UNet: A Nested UNet for Histopathology Image Segmentation](https://mlsanity.com/paper/2304.04567/) (2023)
- [Graph Your Own Prompt](https://mlsanity.com/paper/2509.23373/) (2025)
- [Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)](https://mlsanity.com/paper/2103.03759/) (2021)
- [Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images](https://mlsanity.com/paper/2205.15941/) (2022)
- [Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation](https://mlsanity.com/paper/2403.09942/) (2024)
- [LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors](https://mlsanity.com/paper/2603.07228/) (2026)
- [Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation](https://mlsanity.com/paper/2106.11284/) (2021)
- [DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.05352/) (2021)
- [A Review on The Division of Magnetic Resonant Prostate Images with Deep Learning](https://mlsanity.com/paper/2111.10683/) (2021)
- [ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation](https://mlsanity.com/paper/2206.05225/) (2022)
- [Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures](https://mlsanity.com/paper/2207.09483/) (2022)
- [Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation](https://mlsanity.com/paper/2211.10588/) (2022)
- [Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer](https://mlsanity.com/paper/2302.01493/) (2023)
- [Learnable Weight Initialization for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2306.09320/) (2023)
- [Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores](https://mlsanity.com/paper/2308.04653/) (2023)
- [FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation](https://mlsanity.com/paper/2309.01322/) (2023)
- [Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans](https://mlsanity.com/paper/2404.11152/) (2024)
- [RefineFormer3D: Efficient 3D Medical Image Segmentation via Adaptive Multi-Scale Transformer with Cross Attention Fusion](https://mlsanity.com/paper/2602.16320/) (2026)

## Abstract (arXiv)

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.