Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method.

Background: When assessing the volume of pulmonary nodules on computed tomography (CT) images, there is an inevitable discrepancy between values based on the diameter-based volume calculation and the voxel-counting method, which is derived from the Euclidean distance measurement method on pixel/voxel-based digital image. We aimed to evaluate the ability of a modified diameter measurement method to reduce the discrepancy, and we determined a conversion equation to equate volumes derived from different methods.

Methods: Two different anthropomorphic phantoms with subsolid and solid nodules were repeatedly scanned under various settings.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers.

We aimed to analyse the CT examinations of the previous screening round (CT) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CT in participants with incidence lung cancer, and a DL-CAD analysed CT according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers.

Performance of a Deep Learning Algorithm in Detecting Osteonecrosis of the Femoral Head on Digital Radiography: A Comparison With Assessments by Radiologists.

The objective of our study was to compare the sensitivity of a deep learning (DL) algorithm with the assessments by radiologists in diagnosing osteonecrosis of the femoral head (ONFH) using digital radiography. We performed a two-center, retrospective, noninferiority study of consecutive patients (≥ 16 years old) with a diagnosis of ONFH based on MR images. We investigated the following four datasets of unilaterally cropped hip anteroposterior radiographs: training ( = 1346), internal validation ( = 148), temporal external test ( = 148), and geographic external test ( = 250).

Development of an algorithm to automatically compress a CT image to visually lossless threshold.

Background: To develop an algorithm to predict the visually lossless thresholds (VLTs) of CT images solely using the original images by exploiting the image features and DICOM header information for JPEG2000 compression and to evaluate the algorithm in comparison with pre-existing image fidelity metrics.

Methods: Five radiologists independently determined the VLT for 206 body CT images for JPEG2000 compression using QUEST procedure. The images were divided into training (n = 103) and testing (n = 103) sets.

Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography.

Objectives: The aim of this study was to compare the diagnostic performance of a deep learning algorithm with that of radiologists in diagnosing maxillary sinusitis on Waters' view radiographs.

Materials And Methods: Among 80,475 Waters' view radiographs, examined between May 2003 and February 2017, 9000 randomly selected cases were classified as normal or maxillary sinusitis based on radiographic findings and divided into training (n = 8000) and validation (n = 1000) sets to develop a deep learning algorithm. Two test sets composed of Waters' view radiographs with concurrent paranasal sinus computed tomography were labeled based on computed tomography findings: one with temporal separation (n = 140) and the other with geographic separation (n = 200) from the training set.