# USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

arXiv: https://arxiv.org/abs/1904.08254 · HTML: https://mlsanity.com/paper/1904.08254/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 27 of 27)

- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction](https://mlsanity.com/paper/2101.03244/) (2021)
- [Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation](https://mlsanity.com/paper/2206.01369/) (2022)
- [Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy](https://mlsanity.com/paper/2105.07467/) (2021)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI](https://mlsanity.com/paper/2203.15163/) (2022)
- [Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images](https://mlsanity.com/paper/2103.12650/) (2021)
- [ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans](https://mlsanity.com/paper/2211.13238/) (2022)
- [Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond](https://mlsanity.com/paper/2104.03840/) (2021)
- [MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets](https://mlsanity.com/paper/2307.02100/) (2023)
- [CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.04942/) (2023)
- [SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography](https://mlsanity.com/paper/2103.06419/) (2021)
- [Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning](https://mlsanity.com/paper/2201.06358/) (2022)
- [Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data](https://mlsanity.com/paper/2211.02400/) (2022)
- [UltraUNet: Real-Time Ultrasound Tongue Segmentation for Diverse Linguistic and Imaging Conditions](https://mlsanity.com/paper/2509.23225/) (2025)
- [COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty](https://mlsanity.com/paper/2201.03053/) (2022)
- [Computer-Assisted Analysis of Biomedical Images](https://mlsanity.com/paper/2106.04381/) (2021)
- [Focal Attention Networks: optimising attention for biomedical image segmentation](https://mlsanity.com/paper/2111.00534/) (2021)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.06697/) (2022)
- [Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures](https://mlsanity.com/paper/2207.09483/) (2022)
- [Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT](https://mlsanity.com/paper/2211.05942/) (2022)
- [Scale-Space Hypernetworks for Efficient Biomedical Imaging](https://mlsanity.com/paper/2304.05448/) (2023)
- [Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation](https://mlsanity.com/paper/2305.07850/) (2023)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)
- [ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning](https://mlsanity.com/paper/2601.08732/) (2026)
- [Focus on What Matters: Two-Stage ROI-Aware Refinement for Anatomy-Preserving Fetal Ultrasound Reconstruction](https://mlsanity.com/paper/2604.23839/) (2026)

## Abstract (arXiv)

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training.