Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image

• URL: http://arxiv.org/abs/2407.20495v2
• Date: Wed, 28 Aug 2024 07:40:18 GMT
• Title: Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image
• Authors: Yi Gu, Yoshito Otake, Keisuke Uemura, Masaki Takao, Mazen Soufi, Seiji Okada, Nobuhiko Sugano, Hugues Talbot, Yoshinobu Sato,
• Abstract summary: This research aims to improve quantitative image synthesis (QIS) by exploring pretraining and image resolution scaling. We propose a benchmark for evaluating pretraining performance using the task of QIS-based bone mineral density estimation from plain X-ray images.
• Score: 7.832005676209272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While most vision tasks are essentially visual in nature (for recognition), some important tasks, especially in the medical field, also require quantitative analysis (for quantification) using quantitative images. Unlike in visual analysis, pixel values in quantitative images correspond to physical metrics measured by specific devices (e.g., a depth image). However, recent work has shown that it is sometimes possible to synthesize accurate quantitative values from visual ones (e.g., depth from visual cues or defocus). This research aims to improve quantitative image synthesis (QIS) by exploring pretraining and image resolution scaling. We propose a benchmark for evaluating pretraining performance using the task of QIS-based bone mineral density (BMD) estimation from plain X-ray images, where the synthesized quantitative image is used to derive BMD. Our results show that appropriate pretraining can improve QIS performance, significantly raising the correlation of BMD estimation from 0.820 to 0.898, while others do not help or even hinder it. Scaling-up the resolution can further boost the correlation up to 0.923, a significant enhancement over conventional methods. Future work will include exploring more pretraining strategies and validating them on other image synthesis tasks.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• mmID: High-Resolution mmWave Imaging for Human Identification [16.01613518230451]
  This paper proposes to improve imaging resolution by estimating the human figure as a whole using conditional generative adversarial networks (cGAN) Our system generates environmentally independent, high-resolution images that can extract unique physical features useful for human identification. Our finding indicates high-resolution accuracy with 5% mean silhouette difference to the Kinect device.
  arXiv  Detail & Related papers  (2024-02-01T20:19:38Z)
• 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)
• Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs [8.596819713822477]
  We present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal plexuses and 2) a suite of physics-based image augmentations.
  arXiv  Detail & Related papers  (2022-07-22T14:22:22Z)
• Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants [58.720142291102135]
  This study develops an algorithm for calculating bone growth in contact with metallic implants. Bone and implant tissue were manually segmented in the training data set. In terms of network accuracy, the model reached around 98%.
  arXiv  Detail & Related papers  (2022-04-22T08:17:20Z)
• Image Quality Assessment for Magnetic Resonance Imaging [4.05136808278614]
  Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the image quality. We use outputs of neural network models trained to solve problems relevant to MRI. Seven trained radiologists assess distorted images, with their verdicts then correlated with 35 different image quality metrics.
  arXiv  Detail & Related papers  (2022-03-15T11:52:29Z)
• High Quality Segmentation for Ultra High-resolution Images [72.97958314291648]
  We propose the Continuous Refinement Model for the ultra high-resolution segmentation refinement task. Our proposed method is fast and effective on image segmentation refinement.
  arXiv  Detail & Related papers  (2021-11-29T11:53:06Z)
• Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis [77.34726150561087]
  PAC-Bayes flatness-based and path norm-based measures produce the most consistent explanation for the combination of models and data. We also investigate the use of multi-task classification and segmentation approach for breast images.
  arXiv  Detail & Related papers  (2021-03-04T20:58:22Z)
• Multi-view Human Pose and Shape Estimation Using Learnable Volumetric Aggregation [0.0]
  We propose a learnable aggregation approach to reconstruct 3D human body pose and shape from calibrated multi-view images. Compared to previous approaches, our framework shows higher accuracy and greater promise for real-time prediction, given its cost efficiency.
  arXiv  Detail & Related papers  (2020-11-26T18:33:35Z)
• Unified Representation Learning for Efficient Medical Image Analysis [0.623075162128532]
  We propose a multi-task training approach for medical image analysis using a unified modality-specific feature representation (UMS-Rep) Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance.
  arXiv  Detail & Related papers  (2020-06-19T16:52:16Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.