AIMC Topic: Tomography, X-Ray Computed

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HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The segmentation and visualization of liver vessels in 3D CT images are essential for computer-aided diagnosis and preoperative planning of liver diseases. Due to the irregular structure of liver vessels and image noise, acc...

Applicability analysis of immunotherapy for lung cancer patients based on deep learning.

Methods (San Diego, Calif.)
According to global and Chinese cancer statistics, lung cancer is the second most common cancer globally with the highest mortality rate and a severe threat to human life and health. In recent years, immunotherapy has made significant breakthroughs i...

Automatic segmentation of thoracic CT images using three deep learning models.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: Deep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. ...

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.

Annals of surgical oncology
BACKGROUND: High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation compo...

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Radiology
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To...

Machine learning-enabled resolution-lossless tomography for composite structures with a restricted sensing capability.

Ultrasonics
Construction of a precise ultrasound tomographic image is guaranteed only when the sensor network for signal acquisition is of adequate density. On the other hand, machine learning (ML), as represented by artificial neural network and convolutional n...

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

European radiology
OBJECTIVES: While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for...

Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography.

European journal of radiology
PURPOSE: The purpose of this study was to evaluate the performance of a deep learning system for the automatic diagnosis and classification of rib fractures.