The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

• URL: http://arxiv.org/abs/2305.09011v6
• Date: Sun, 24 Nov 2024 23:47:20 GMT
• Title: The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
• Authors: Hongwei Bran Li, Gian Marco Conte, Qingqiao Hu, Syed Muhammad Anwar, Florian Kofler, Ivan Ezhov, Koen van Leemput, Marie Piraud, Maria Diaz, Byrone Cole, Evan Calabrese, Jeff Rudie, Felix Meissen, Maruf Adewole, Anastasia Janas, Anahita Fathi Kazerooni, Dominic LaBella, Ahmed W. Moawad, Keyvan Farahani, James Eddy, Timothy Bergquist, Verena Chung, Russell Takeshi Shinohara, Farouk Dako, Walter Wiggins, Zachary Reitman, Chunhao Wang, Xinyang Liu, Zhifan Jiang, Ariana Familiar, Elaine Johanson, Zeke Meier, Christos Davatzikos, John Freymann, Justin Kirby, Michel Bilello, Hassan M. Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Rivka R. Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc André Weber, Abhishek Mahajan, Suyash Mohan, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva, Meyer Errol Colak, Priscila Crivellaro, Andras Jakab, Jake Albrecht, Udunna Anazodo, Mariam Aboian, Thomas Yu, Verena Chung, Timothy Bergquist, James Eddy, Jake Albrecht, Ujjwal Baid, Spyridon Bakas, Marius George Linguraru, Bjoern Menze, Juan Eugenio Iglesias, Benedikt Wiestler,
• Abstract summary: We present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided.
• Score: 9.082208613256295
• License:
• Abstract: Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Related papers

• An Ensemble Approach for Brain Tumor Segmentation and Synthesis [0.12777007405746044]
  The integration of machine learning in magnetic resonance imaging (MRI) is proving to be incredibly effective. Deep learning models utilize multiple layers of processing to capture intricate details of complex data. We propose a deep learning framework that ensembles state-of-the-art architectures to achieve accurate segmentation.
  arXiv  Detail & Related papers  (2024-11-26T17:28:51Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation [51.28453192441364]
  Multimodal brain magnetic resonance (MR) imaging is indispensable in neuroscience and neurology. Current MR image synthesis approaches are typically trained on independent datasets for specific tasks. We present TUMSyn, a Text-guided Universal MR image Synthesis model, which can flexibly generate brain MR images.
  arXiv  Detail & Related papers  (2024-09-25T11:14:47Z)
• Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection [1.9461727843485295]
  Gliomas present significant surgical challenges due to similarity to healthy tissue. MRI images are often ineffective during surgery due to factors such as brain shift. This paper presents a deep learning pipeline combining You Only Look Once Version 8 (Yv8) and Segment Anything Model Vision Transformer-base.
  arXiv  Detail & Related papers  (2024-08-27T07:58:08Z)
• Enhanced Synthetic MRI Generation from CT Scans Using CycleGAN with Feature Extraction [3.2088888904556123]
  We propose an approach for enhanced monomodal registration using synthetic MRI images from CT scans. Our methodology shows promising results, outperforming several state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-31T16:39:56Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding [51.911473457195555]
  BrainCLIP is a task-agnostic fMRI-based brain decoding model. It bridges the modality gap between brain activity, image, and text. BrainCLIP can reconstruct visual stimuli with high semantic fidelity.
  arXiv  Detail & Related papers  (2023-02-25T03:28:54Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities [2.867517731896504]
  Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. It's common to miss some imaging modalities in clinical practice. We present a novel brain tumor segmentation algorithm with missing modalities.
  arXiv  Detail & Related papers  (2021-04-13T14:21:09Z)
• Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis [30.64847799586407]
  We propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesized images is better than the one synthesized by the baseline model, pix2pix.
  arXiv  Detail & Related papers  (2020-05-02T21:33:15Z)

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.