Similarity Metrics for MR Image-To-Image Translation
- URL: http://arxiv.org/abs/2405.08431v3
- Date: Tue, 18 Jun 2024 08:26:56 GMT
- Title: Similarity Metrics for MR Image-To-Image Translation
- Authors: Melanie Dohmen, Mark Klemens, Ivo Baltruschat, Tuan Truong, Matthias Lenga,
- Abstract summary: We give an overview and a quantitative analysis of 15 metrics for assessing the quality of synthetically generated images.
We include 11 full-reference metrics (SSIM, MS-SSIM, CW-SSIM, PSNR, MSE, NMSE, MAE, LPIPS, DISTS, NMI and PCC), three non-reference metrics (BLUR, MLC, MSLC) and one downstream task segmentation metric (DICE)
We analyze the influence of four prominent normalization methods (Minmax, cMinmax, Zscore and Quantile) on the different metrics and distortions.
- Score: 0.8932296777085644
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Image-to-image translation can create large impact in medical imaging, for instance the possibility to synthetically transform images to other modalities, sequence types, higher resolutions or lower noise levels. In order to assure a high level of patient safety, these methods are mostly validated by human reader studies, which require a considerable amount of time and costs. Quantitative metrics have been used to complement such studies and to provide reproducible and objective assessment of synthetic images. Even though the SSIM and PSNR metrics are extensively used, they do not detect all types of errors in synthetic images as desired. Other metrics could provide additional useful evaluation. In this study, we give an overview and a quantitative analysis of 15 metrics for assessing the quality of synthetically generated images. We include 11 full-reference metrics (SSIM, MS-SSIM, CW-SSIM, PSNR, MSE, NMSE, MAE, LPIPS, DISTS, NMI and PCC), three non-reference metrics (BLUR, MLC, MSLC) and one downstream task segmentation metric (DICE) to detect 11 kinds of typical distortions and artifacts that occur in MR images. In addition, we analyze the influence of four prominent normalization methods (Minmax, cMinmax, Zscore and Quantile) on the different metrics and distortions. Finally, we provide adverse examples to highlight pitfalls in metric assessment and derive recommendations for effective usage of the analyzed similarity metrics for evaluation of image-to-image translation models.
Related papers
- Non-Reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs [0.0]
This study introduces a novel deep learning-based non-reference approach to assess brain MRI quality by training a 3D ResNet.
The network is designed to estimate quality across six distinct artifacts commonly encountered in MRI scans.
Results demonstrate superior performance in accurately estimating distortions and reflecting image quality from multiple perspectives.
arXiv Detail & Related papers (2024-07-20T22:05:30Z) - Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images [0.7499722271664147]
The Global-Local Image Perceptual Score (GLIPS) is an image metric designed to assess the photorealistic image quality of AI-generated images.
Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, SSIM, and MS-SSIM in terms of correlation with human scores.
arXiv Detail & Related papers (2024-05-15T15:19:23Z) - CrossScore: Towards Multi-View Image Evaluation and Scoring [24.853612457257697]
Cross-reference image quality assessment method fills the gap in the image assessment landscape.
Our method enables accurate image quality assessment without requiring ground truth references.
arXiv Detail & Related papers (2024-04-22T17:59:36Z) - 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) - Evaluating the Quality and Diversity of DCGAN-based Generatively
Synthesized Diabetic Retinopathy Imagery [0.07499722271664144]
Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR.
The imbalance can be addressed using Geneversarative Adrial Networks (GANs) to augment the datasets with synthetic images.
To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr't Inception Distance (FID) are used.
arXiv Detail & Related papers (2022-08-10T23:50:01Z) - 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) - Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2022-03-29T08:41:17Z) - Symmetry-Enhanced Attention Network for Acute Ischemic Infarct
Segmentation with Non-Contrast CT Images [50.55978219682419]
We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation.
Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric.
The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
arXiv Detail & Related papers (2021-10-11T07:13:26Z) - Towards Ultrafast MRI via Extreme k-Space Undersampling and
Superresolution [65.25508348574974]
We go below the MRI acceleration factors reported by all published papers that reference the original fastMRI challenge.
We consider powerful deep learning based image enhancement methods to compensate for the underresolved images.
The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor.
arXiv Detail & Related papers (2021-03-04T10:45:01Z) - Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes
from clinical MRI exams with scans of different orientation, resolution and
contrast [4.987889348212769]
We present SynthSR, a method to train a CNN that receives one or more thick-slice scans with different contrast, resolution and orientation.
The presented method does not require any preprocessing, e.g., stripping or bias field correction.
arXiv Detail & Related papers (2020-12-24T17:29:53Z)
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.