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Found 18 papers, 1 papers with code
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study
Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes.
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize.
Weakly-Supervised Detection of Bone Lesions in CT
First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks.
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging.
Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors
Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia.
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study
This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
Semantic Image Synthesis for Abdominal CT
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset.
Universal Lymph Node Detection in T2 MRI using Neural Networks
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
Universal Lesion Detection in CT Scans using Neural Network Ensembles
In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions.
Lymph Node Detection in T2 MRI with Transformers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning
Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners.
Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI
In this paper, we seek to quantify the bias in terms of the impact that different levels of motion artifacts have on the performance of neural networks engaged in a lesion segmentation task.
Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images
To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface.
Learning to Segment Corneal Tissue Interfaces in OCT Images
To the best of our knowledge, this is the first deep learning based approach to segment both anterior and posterior corneal tissue interfaces.
Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand.
