From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation

• URL: http://arxiv.org/abs/2606.22168v1
• Date: Sat, 20 Jun 2026 17:57:12 GMT
• Title: From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation
• Abstract summary: This paper presents a comparative study of five U-Net based architectures: U-Net 3D, Residual U-Net, Attention U-Net, UNETR, and Swin UNETR.<n>Results show that Swin UNETR achieves the best overall performance, with Dice scores of 0.8965 on BraTS 2023 and 0.8078 on DRIVE.
• Score: 4.432419259191087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used for biomedical image segmentation because of its encoder decoder structure and skip connections. However, conventional convolution based U-Net models may have limited ability to capture long range dependencies and global contextual information, which can affect performance in complex segmentation tasks. This paper presents a comparative study of five U-Net based architectures: U-Net 3D, Residual U-Net, Attention U-Net, UNETR, and Swin UNETR. The models are evaluated on two benchmark datasets: BraTS 2023 for brain tumor segmentation and DRIVE for retinal vessel segmentation. Experimental results show that Swin UNETR achieves the best overall performance, with Dice scores of 0.8965 on BraTS 2023 and 0.8078 on DRIVE. The results suggest that transformer based U-Net variants are effective for segmentation tasks requiring global contextual modeling, while residual learning remains useful for fine structure segmentation. This study provides practical insights into model selection for medical image segmentation across volumetric MRI and retinal imaging tasks.

Related papers

• A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging [0.0]
  This work presents a unified benchmark for comparing representative convolutional neural networks (CNNs) and Transformer-based models.<n>Five state-of-the-art three-dimensional segmentation models, including 3D U-Net, SegResNet, Swin UNETR, SegMamba, and SegMambaV2, are evaluated.<n>Performance is assessed using segmentation accuracy metrics together with computational cost indicators, including inference time and the size of each model.
  arXiv  Detail & Related papers  (2026-07-30T21:56:03Z)
• MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network [51.68708264694361]
  Confusion factors can affect medical images, such as complex anatomical variations and imaging modality limitations.<n>We propose a multi-causal aware modeling backdoor-intervention optimization network for medical image segmentation.<n>Our method significantly reduces the influence of confusion factors, leading to enhanced segmentation accuracy.
  arXiv  Detail & Related papers  (2025-05-28T01:40:10Z)
• Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan [5.5193366921929155]
  We evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, SAM 2.<n>The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions.<n> Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest.
  arXiv  Detail & Related papers  (2025-04-05T05:47:56Z)
• TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI [59.86827659781022]
  A nnU-Net model (TotalSegmentator) was trained on MRI and segment 80atomic structures.<n>Dice scores were calculated between the predicted segmentations and expert reference standard segmentations to evaluate model performance.<n>Open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures.
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• 3D Medical Image Segmentation based on multi-scale MPU-Net [5.393743755706745]
  This paper proposes a tumor segmentation model MPU-Net for patient volume CT images. It is inspired by Transformer with a global attention mechanism. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results.
  arXiv  Detail & Related papers  (2023-07-11T20:46:19Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Learning from partially labeled data for multi-organ and tumor segmentation [102.55303521877933]
  We propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple datasets. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. We create a large-scale partially labeled Multi-Organ and Tumor benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors.
  arXiv  Detail & Related papers  (2022-11-13T13:03:09Z)
• Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices [0.5735035463793008]
  We present the implementation of Modified U-Net on Intel Movidius Neural Compute Stick 2 (NCS-2) for the segmentation of medical images. Experiments are reported for segmentation task on three medical imaging datasets: BraTs dataset of brain MRI, heart MRI dataset, and ZNSDB dataset.
  arXiv  Detail & Related papers  (2022-06-06T05:25:19Z)
• Category Guided Attention Network for Brain Tumor Segmentation in MRI [6.685945448824158]
  We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net) In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity.
  arXiv  Detail & Related papers  (2022-03-29T09:22:29Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
  We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net) We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
  arXiv  Detail & Related papers  (2020-08-06T15:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.