Efficient brain age prediction from 3D MRI volumes using 2D projections

• URL: http://arxiv.org/abs/2211.05762v1
• Date: Thu, 10 Nov 2022 18:50:10 GMT
• Title: Efficient brain age prediction from 3D MRI volumes using 2D projections
• Authors: Johan J\"onemo, Muhammad Usman Akbar, Robin K\"ampe, J Paul Hamilton, Anders Eklund
• Abstract summary: We show that using 2D CNNs on a few 2D projections leads to reasonable test accuracy when predicting the age from brain volumes. One training epoch with 20,324 subjects takes 40 - 70 seconds using a single GPU, which is almost 100 times faster compared to a small 3D CNN.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 40 - 70 seconds using a single GPU, which is almost 100 times faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.

Related papers

• Video Pretraining Advances 3D Deep Learning on Chest CT Tasks [63.879848037679224]
  Pretraining on large natural image classification datasets has aided model development on data-scarce 2D medical tasks. These 2D models have been surpassed by 3D models on 3D computer vision benchmarks. We show video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks.
  arXiv  Detail & Related papers  (2023-04-02T14:46:58Z)
• Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis [0.0]
  We present a simple yet effective 2D method to handle 3D data while efficiently embedding the 3D knowledge during training. Our method generates a super-resolution image by stitching slices side by side in the 3D image. While attaining equal, if not superior, results to 3D networks utilizing only 2D counterparts, the model complexity is reduced by around threefold.
  arXiv  Detail & Related papers  (2022-05-05T09:59:03Z)
• MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification [59.10015984688104]
  MedMNIST v2 is a large-scale MNIST-like dataset collection of standardized biomedical images. The resulting dataset consists of 708,069 2D images and 10,214 3D images in total.
  arXiv  Detail & Related papers  (2021-10-27T22:02:04Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Continual 3D Convolutional Neural Networks for Real-time Processing of Videos [93.73198973454944]
  We introduce Continual 3D Contemporalal Neural Networks (Co3D CNNs) Co3D CNNs process videos frame-by-frame rather than by clip by clip. We show that Co3D CNNs initialised on the weights from preexisting state-of-the-art video recognition models reduce floating point operations for frame-wise computations by 10.0-12.4x while improving accuracy on Kinetics-400 by 2.3-3.8x.
  arXiv  Detail & Related papers  (2021-05-31T18:30:52Z)
• Improved Brain Age Estimation with Slice-based Set Networks [18.272915375351914]
  We propose a new architecture for BrainAGE prediction. The proposed architecture works by encoding each 2D slice in an MRI with a deep 2D-CNN model. Next, it combines the information from these 2D-slice encodings using set networks or permutation invariant layers. Experiments on the BrainAGE prediction problem, using the UK Biobank dataset, showed that the model with the permutation invariant layers trains faster and provides better predictions compared to other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-02-08T18:54:15Z)
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [93.51773847125014]
  We propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. Our approach performs 3D convolutions across space and time over a bird's eye view representation of the 3D world.
  arXiv  Detail & Related papers  (2020-12-22T22:43:35Z)
• Dynamic Image for 3D MRI Image Alzheimer's Disease Classification [26.296422774282156]
  Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. Our proposed CNN model achieves $9.5%$ better Alzheimer's disease classification accuracy than the baseline 3D models.
  arXiv  Detail & Related papers  (2020-11-30T21:39:32Z)
• 3D Deep Learning on Medical Images: A Review [0.691592786984092]
  We trace the history of how the 3D CNN was developed from its machine learning roots. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs.
  arXiv  Detail & Related papers  (2020-04-01T03:56:48Z)
• 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data [54.223614679807994]
  3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks. Applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. In this work, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. As a result, 2D CNN networks can also be used to learn volumetric information.
  arXiv  Detail & Related papers  (2020-02-11T08:24:19Z)

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.