MELAGE: A purely python based Neuroimaging software (Neonatal)

• URL: http://arxiv.org/abs/2309.07175v1
• Date: Tue, 12 Sep 2023 19:54:35 GMT
• Title: MELAGE: A purely python based Neuroimaging software (Neonatal)
• Authors: Bahram Jafrasteh, Sim\'on Pedro Lubi\'an L\'opez, Isabel Benavente Fern\'andez
• Abstract summary: MELAGE, a pioneering Python-based neuroimaging software, emerges as a versatile tool for the visualization, processing, and analysis of medical images. Initially conceived to address the unique challenges of processing 3D ultrasound and MRI brain images during the neonatal period, MELAGE exhibits remarkable adaptability.
• Score: 1.3654846342364308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: MELAGE, a pioneering Python-based neuroimaging software, emerges as a versatile tool for the visualization, processing, and analysis of medical images. Initially conceived to address the unique challenges of processing 3D ultrasound and MRI brain images during the neonatal period, MELAGE exhibits remarkable adaptability, extending its utility to the domain of adult human brain imaging. At its core, MELAGE features a semi-automatic brain extraction tool empowered by a deep learning module, ensuring precise and efficient brain structure extraction from MRI and 3D Ultrasound data. Moreover, MELAGE offers a comprehensive suite of features, encompassing dynamic 3D visualization, accurate measurements, and interactive image segmentation. This transformative software holds immense promise for researchers and clinicians, offering streamlined image analysis, seamless integration with deep learning algorithms, and broad applicability in the realm of medical imaging.

Related papers

• Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI [4.453300553789746]
  We propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI) Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer's disease classification, Parkinson's disease classification, and age prediction tasks.
  arXiv  Detail & Related papers  (2024-10-01T05:21:02Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework. Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Mask-Guided Attention U-Net for Enhanced Neonatal Brain Extraction and Image Preprocessing [0.9674145073701153]
  We introduce MGA-Net, a novel mask-guided attention neural network. It is designed to extract the brain from other structures and reconstruct high-quality brain images. We extensively validated the proposed MGA-Net on diverse datasets from varied clinical settings and neonatal age groups.
  arXiv  Detail & Related papers  (2024-06-25T16:48:18Z)
• Brain3D: Generating 3D Objects from fMRI [76.41771117405973]
  We design a novel 3D object representation learning method, Brain3D, that takes as input the fMRI data of a subject. We show that our model captures the distinct functionalities of each region of human vision system. Preliminary evaluations indicate that Brain3D can successfully identify the disordered brain regions in simulated scenarios.
  arXiv  Detail & Related papers  (2024-05-24T06:06:11Z)
• Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction [8.63068449082585]
  Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition. Our framework integrates 3D brain structures with visual semantics using a Vision Transformer 3D. We have enhanced the fMRI dataset with diverse fMRI-image-related textual data to support multimodal large model development.
  arXiv  Detail & Related papers  (2024-04-30T10:41:23Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• 3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation [6.127298607534532]
  Deep learning has recently emerged to improve brain tumor segmentation. Transformers have been leveraged to address the limitations of convolutional networks. We propose a 3D Transformer-based segmentation approach.
  arXiv  Detail & Related papers  (2023-04-28T02:11:29Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction [13.44786774177579]
  We establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging.
  arXiv  Detail & Related papers  (2021-05-25T06:20:03Z)

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.