Deep Learning for Musculoskeletal Image Analysis
- URL: http://arxiv.org/abs/2003.00541v1
- Date: Sun, 1 Mar 2020 18:13:59 GMT
- Title: Deep Learning for Musculoskeletal Image Analysis
- Authors: Ismail Irmakci, Syed Muhammad Anwar, Drew A. Torigian, and Ulas Bagci
- Abstract summary: This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans.
We study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears.
Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances.
- Score: 5.271212551436945
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The diagnosis, prognosis, and treatment of patients with musculoskeletal
(MSK) disorders require radiology imaging (using computed tomography, magnetic
resonance imaging(MRI), and ultrasound) and their precise analysis by expert
radiologists. Radiology scans can also help assessment of metabolic health,
aging, and diabetes. This study presents how machinelearning, specifically deep
learning methods, can be used for rapidand accurate image analysis of MRI
scans, an unmet clinicalneed in MSK radiology. As a challenging example, we
focus on automatic analysis of knee images from MRI scans and study machine
learning classification of various abnormalities including meniscus and
anterior cruciate ligament tears. Using widely used convolutional neural
network (CNN) based architectures, we comparatively evaluated the knee
abnormality classification performances of different neural network
architectures under limited imaging data regime and compared single and
multi-view imaging when classifying the abnormalities. Promising results
indicated the potential use of multi-view deep learning based classification of
MSK abnormalities in routine clinical assessment.
Related papers
- Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI [3.639043225506316]
We introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans.
Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions.
We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans.
arXiv Detail & Related papers (2024-09-05T19:52:09Z) - Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs [1.779948689352186]
We analyse the capabilities of different configurations of Deep Learning models to generate synthetic CT scans from MRI.
Several CycleGAN models were trained unsupervised to generate CT scans from different MRI modalities with and without contrast agents.
The results show how, depending on the input modalities, the models can have very different performances.
arXiv Detail & Related papers (2024-07-15T16:38:59Z) - 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) - K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality
Assessment [71.27193056354741]
The problem of how to assess cross-modality medical image synthesis has been largely unexplored.
We propose a new metric K-CROSS to spur progress on this challenging problem.
K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
arXiv Detail & Related papers (2023-07-10T01:26:48Z) - On Sensitivity and Robustness of Normalization Schemes to Input
Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input.
DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases.
We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
arXiv Detail & Related papers (2023-06-23T03:09:03Z) - 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) - Automated SSIM Regression for Detection and Quantification of Motion
Artefacts in Brain MR Images [54.739076152240024]
Motion artefacts in magnetic resonance brain images are a crucial issue.
The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis.
An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
arXiv Detail & Related papers (2022-06-14T10:16:54Z) - Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and
Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods.
We will detail the research in coupling physics and data driven models for MRI acceleration.
Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
arXiv Detail & Related papers (2022-04-01T22:48:08Z) - SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image
Labels for Quantitative Clinical Evaluation [5.37260403457093]
We present the Stanford Knee MRI with Multi-Task Evaluation dataset, a collection of quantitative knee MRI (qMRI) scans.
This dataset consists of raw-data measurements of 25,000 slices (155 patients) of anonymized patient MRI scans.
We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques.
arXiv Detail & Related papers (2022-03-14T02:40:40Z) - 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) - Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo
Hyperspectral Tumor Type Classification [49.32653090178743]
We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning.
Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
arXiv Detail & Related papers (2020-07-02T12:00:53Z)
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.