Applications of Synthetic Data Integration for Deep Learning for Volumetric Analysis and Segmentation in Thoracic CT Imaging
Zeyrek, A.; Navarro, S. M.
Show abstract
This study presents a framework for processing Digital Imaging and Communications in Medicine (DICOM) medical imaging data by integrating synthetic objects for volumetric analysis and simulation for applications in assessment of computed tomography (CT) imaging used in thoracic surgery. Functions are designed to generate synthetic objects including geometric shapes such as spheres, cubes, rectangular prisms, cylinders, and blobs with known volumes. Validation is performed through test functions to ensure accuracy and consistency. Additionally, the use of UNet models for segmenting various chest pathologies, such as hemothorax and pneumothorax, as well as organs, is demonstrated. The created framework is used to generate synthetic data to address the scarcity of publicly available hemothorax CT imaging data. Models achieved high performance, assessed by various metrics. The framework and models provide a robust tool for data augmentation and analysis in medical imaging, potentially enhancing clinical decision-making and supporting research in thoracic surgery and related fields.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation 98%
- Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet 96%
- Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray images 96%
Similar papers in this journal
- tUbe net: a generalisable deep learning tool for 3D vessel segmentation 93%
- Deep Learning Enhances Detection of Extracapsular Extension in Prostate Cancer from mpMRI of 1001 Patients 92%
- Navigating the Multiverse: A Hitchhiker’s Guide to Selecting Harmonisation Methods for Multimodal Biomedical Data 92%
Similar papers in this journal
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 98%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 96%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 95%
Similar papers in this journal
- The Effect of Image Resolution on Automated Classification of Chest X-rays 97%
- A 3D CNN Classification Model for Accurate Diagnosis of Coronavirus Disease 2019 using Computed Tomography Images 97%
- Fiberscopic Pattern Removal for Optimal Coverage in 3D Bladder Reconstructions of Fiberscope Cystoscopy Videos 95%
Similar papers in this journal
- Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs 95%
- Choice of Intraoperative Ultrasound adjuncts for Brain Tumor Surgery 92%
- Towards a Clinically-based Common Coordinate Framework for the Human Gut Cell Atlas - The Gut Models 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.