Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis

• URL: http://arxiv.org/abs/2602.15067v1
• Date: Sat, 14 Feb 2026 07:48:58 GMT
• Title: Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
• Authors: Rut Pate, Snehal Rajput, Mehul S. Raval, Rupal A. Kapdi, Mohendra Roy,
• Abstract summary: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology.<n>This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation.
• Score: 0.815557531820863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation. The proposed model enhances feature representation and segmentation accuracy by integrating residual, recurrent, and triplanar architectures while maintaining computational efficiency, potentially aiding in better treatment planning. The proposed method achieves a Dice Similarity Score (DSC) of 0.900 for Whole Tumor (WT) segmentation on the BraTS2021 validation set, demonstrating performance comparable to leading models. Additionally, the triplanar network extracts 64 features per planar model for survival days prediction, which are reduced to 28 using an Artificial Neural Network (ANN). This approach achieves an accuracy of 45.71%, a Mean Squared Error (MSE) of 108,318.128, and a Spearman Rank Correlation Coefficient (SRC) of 0.338 on the test dataset.

Related papers

• Advancing Brain Tumor Segmentation via Attention-based 3D U-Net Architecture and Digital Image Processing [0.0]
  This study aims to enhance the performance of brain tumor segmentation, ultimately improving the reliability of diagnosis.<n>The proposed model is thoroughly evaluated and assessed on the BraTS 2020 dataset using various performance metrics to accomplish this goal.
  arXiv  Detail & Related papers  (2025-10-21T22:11:19Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging [2.7205074719266062]
  Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning.<n>We introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net)<n>HANS-Net combines hyperbolic convolutions for hierarchical geometric representation, a wavelet-inspired decomposition module for multi-scale texture learning, and an implicit neural representation branch.
  arXiv  Detail & Related papers  (2025-07-15T13:56:37Z)
• UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion [5.182916707460288]
  We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm.<n>This study proposed a novel 3D brain tumor segmentation network based on the U-Net architecture.
  arXiv  Detail & Related papers  (2025-05-06T12:56:04Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [56.99710477905796]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.<n>This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset [0.0]
  This study introduces a new implementation of the Columbia-University-Net architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation.
  arXiv  Detail & Related papers  (2024-06-19T00:01:01Z)
• propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans [16.135854257728337]
  This study introduces a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency.
  arXiv  Detail & Related papers  (2023-05-29T03:24:02Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections [46.414990784180546]
  Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique. CNNs require large annotated datasets for training. Self-supervised learning strategies can leverage unlabeled data for training.
  arXiv  Detail & Related papers  (2021-05-24T12:27:06Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.