Multimodal CNN networks for brain tumor segmentation in MRI: a BraTS 2022 challenge solution
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and
follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub …
follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub …
A review of deep learning for brain tumor analysis in MRI
FJ Dorfner, JB Patel, J Kalpathy-Cramer… - npj Precision …, 2025 - nature.com
Recent progress in deep learning (DL) is producing a new generation of tools across
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …
Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging-a review
Glioblastomata are the most generally perceived fundamental brain malignant tumors
known as Gliomas, with different shape, size & sub regions. It is hard to segment all three …
known as Gliomas, with different shape, size & sub regions. It is hard to segment all three …
QT-UNet: A self-supervised self-querying all-Transformer U-Net for 3D segmentation
AH Håversen, DP Bavirisetti, GH Kiss… - IEEE Access, 2024 - ieeexplore.ieee.org
With reliable performance, and linear time complexity, Vision Transformers like the Swin
Transformer are gaining popularity in the field of Medical Image Computing (MIC). Examples …
Transformer are gaining popularity in the field of Medical Image Computing (MIC). Examples …
Development of an AI-driven system for neurosurgery with a usability study: a step towards minimal invasive robotics
RA Zeineldin, D Junger, F Mathis-Ullrich… - at …, 2023 - degruyter.com
Recent advances in artificial intelligence have enabled promising applications in
neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a …
neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a …
Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy
Objectives The precise segmentation of atrophic structures remains challenging in
neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork …
neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork …
UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation
Z Hu, Y Sun, L Bian, C Luo, J Zhu, J Zhu, S Li… - Computers in Biology …, 2025 - Elsevier
Gliomas are the most common and malignant form of primary brain tumors. Accurate
segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to …
segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to …
A 3D hierarchical cross‐modality interaction network using transformers and convolutions for brain glioma segmentation in MR images
Y Zhuang, H Liu, W Fang, G Ma, S Sun, Y Zhu… - Medical …, 2024 - Wiley Online Library
Background Precise glioma segmentation from multi‐parametric magnetic resonance (MR)
images is essential for brain glioma diagnosis. However, due to the indistinct boundaries …
images is essential for brain glioma diagnosis. However, due to the indistinct boundaries …
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
RA Zeineldin, F Mathis-Ullrich - arXiv preprint arXiv:2412.08240, 2024 - arxiv.org
Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and
treatment planning across diverse brain tumors. This paper addresses the challenges posed …
treatment planning across diverse brain tumors. This paper addresses the challenges posed …
nnUnetFormer: an automatic method based on nnUnet and transformer for brain tumor segmentation with multimodal MR images
S Guo, Q Chen, L Wang, L Wang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. Both local and global context information is crucial semantic features for brain
tumor segmentation, while almost all the CNN-based methods cannot learn global spatial …
tumor segmentation, while almost all the CNN-based methods cannot learn global spatial …