Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging.

Background: To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training.

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis.

Objectives: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB).

Methods: A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model.

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

Objective: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation.

Methods: The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation.

Metastatic microcalcification in the lungs and stomach due to hyperparathyroidism: A case report.

We present a case of a 34-year-old man who was admitted to our hospital with a six-month history of nausea and vomiting that had worsened over the past two weeks. Diagnosis of primary hyperparathyroidism (PHPT) due to a single parathyroid adenoma was confirmed by laboratory results and imaging but following pharmacological therapy the patient developed a dry cough. Single-photon emission tomography/computed tomography (SPECT/CT) with technetium-99m-diphosphonate (Tc-MDP) was used to assist in the diagnosis and results showed diffuse metastatic microcalcification in the lungs and stomach.

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia.

The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques.