Learning from imperfect training data using a robust loss function: application to brain image segmentation

• URL: http://arxiv.org/abs/2208.04941v1
• Date: Mon, 8 Aug 2022 19:08:32 GMT
• Title: Learning from imperfect training data using a robust loss function: application to brain image segmentation
• Authors: Haleh Akrami, Wenhui Cui, Anand A Joshi, Richard M. Leahy
• Abstract summary: In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures. Here we propose a deep learning framework that can segment brain, skull, and extra-cranial tissue using only T1-weighted MRI as input.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures and is also a necessary step for other applications such as current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). Here we propose a deep learning framework that can segment brain, skull, and extra-cranial tissue using only T1-weighted MRI as input. In addition, we describe a robust method for training the model in the presence of noisy labels.

Related papers

• MindFormer: A Transformer Architecture for Multi-Subject Brain Decoding via fMRI [50.55024115943266]
  We introduce a new Transformer architecture called MindFormer to generate fMRI-conditioned feature vectors. MindFormer incorporates two key innovations: 1) a novel training strategy based on the IP-Adapter to extract semantically meaningful features from fMRI signals, and 2) a subject specific token and linear layer that effectively capture individual differences in fMRI signals.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Segment anything model (SAM) for brain extraction in fMRI studies [0.0]
  We will use the segment anything model (SAM) for neuroimaging brain segmentation by removing skull artifacts. The results of the experiments showed promising results that explore using automated segmentation algorithms for neuroimaging without the need to train on custom medical imaging dataset.
  arXiv  Detail & Related papers  (2024-01-09T06:25:09Z)
• 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)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• SHARM: Segmented Head Anatomical Reference Models [1.3108652488669732]
  This study provides an open-access Segmented Head Anatomical Reference Models (SHARM) that consists of 196 subjects. The SHARM is expected to be a useful benchmark not only for electromagnetic dosimetry studies but also for different human head segmentation applications.
  arXiv  Detail & Related papers  (2023-09-13T02:24:37Z)
• Brain tumor multi classification and segmentation in MRI images using deep learning [3.1248717814228923]
  The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images.
  arXiv  Detail & Related papers  (2023-04-20T01:32:55Z)
• 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)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification [0.6117371161379209]
  We have developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40%. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.
  arXiv  Detail & Related papers  (2021-06-14T12:19:27Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Transfer Learning for Brain Tumor Segmentation [0.6408773096179187]
  Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Recent advances in deep learning have led to convolutional neural network architectures that excel at various visual recognition tasks. In this work, we construct FCNs with pretrained convolutional encoders. We show that we can stabilize the training process this way and achieve an improvement with respect to dice scores and Hausdorff distances.
  arXiv  Detail & Related papers  (2019-12-28T12:45:34Z)

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.