Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants

• URL: http://arxiv.org/abs/2204.10560v1
• Date: Fri, 22 Apr 2022 08:17:20 GMT
• Title: Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants
• Authors: Fernando Garc\'ia-Torres, Carmen M\'inguez-Porter, Julia Tom\'as-Chenoll, Sof\'ia Iranzo-Egea, Juan-Manuel Belda-Lois
• Abstract summary: 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%.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This preliminary study focuses on the development of a medical image segmentation algorithm based on artificial intelligence for calculating bone growth in contact with metallic implants. %as a result of the problem of estimating the growth of new bone tissue due to artifacts. %the presence of various types of distortions and errors, known as artifacts. Two databases consisting of computerized microtomography images have been used throughout this work: 100 images for training and 196 images for testing. Both bone and implant tissue were manually segmented in the training data set. The type of network constructed follows the U-Net architecture, a convolutional neural network explicitly used for medical image segmentation. In terms of network accuracy, the model reached around 98\%. Once the prediction was obtained from the new data set (test set), the total number of pixels belonging to bone tissue was calculated. This volume is around 15\% of the volume estimated by conventional techniques, which are usually overestimated. This method has shown its good performance and results, although it has a wide margin for improvement, modifying various parameters of the networks or using larger databases to improve training.

Related papers

• Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities [0.0]
  This paper investigates the intricacies of using pretrained deep convolutional neural networks with transfer learning across diverse medical imaging datasets. It shows that the use of pretrained models as fixed feature extractors yields poor performance irrespective of the datasets. It is also found that deeper and more complex architectures did not necessarily result in the best performance.
  arXiv  Detail & Related papers  (2024-08-30T04:51:19Z)
• Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging [0.0]
  This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs) The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images.
  arXiv  Detail & Related papers  (2024-02-25T23:49:05Z)
• Deepfake Image Generation for Improved Brain Tumor Segmentation [0.0]
  This work investigates the feasibility of employing deep-fake image generation for effective brain tumor segmentation. A Generative Adversarial Network was used for image-to-image translation and image segmentation using a U-Net-based convolutional neural network trained with deepfake images. Results show improved performance in terms of image segmentation quality metrics, and could potentially assist when training with limited data.
  arXiv  Detail & Related papers  (2023-07-26T16:11:51Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Image Translation for Medical Image Generation -- Ischemic Stroke Lesions [0.0]
  Synthetic databases with annotated pathologies could provide the required amounts of training data. We train different image-to-image translation models to synthesize magnetic resonance images of brain volumes with and without stroke lesions. We show that for a small database of only 10 or 50 clinical cases, synthetic data augmentation yields significant improvement.
  arXiv  Detail & Related papers  (2020-10-05T09:12:28Z)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)
• 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.