Chest X-ray dataset and ground truth for lung segmentation.

Chest X-ray images are a valuable tool for accurately and efficiently diagnosing Covid-19 with the assistance of computer technology. These images enable the detection of diseases in internal organs, particularly the lungs, by providing crucial information about the pathological state of the lungs and other internal organs and tissues. Segmentation plays an essential role in the earliest stages of disease detection through computer-assisted analysis of medical images. This method enables the extraction of significant elements from the image, facilitating the identification of relevant areas. In the subsequent stage, healthcare professionals might acquire more precise diagnosis outcomes. Deep learning plays a significant role in developing models to achieve exact and efficient diagnostic results in picture segmentation and image classification procedures. However, using deep learning models in the image segmentation process necessitates the availability of image datasets and ground truth that radiologists have validated to facilitate the training process. The dataset provided in this article comprises 292 chest X-ray images obtained from Airlangga University Hospital in Indonesia. These images are accompanied with ground truth data that has been meticulously verified by radiologists. The offered X-ray images encompass those of patients diagnosed with Covid-19, pneumonia and those representing normal conditions. The provided dataset exhibits potential utility in advancing artificial intelligence techniques for segmentation and classification procedures.

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative.

Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset. The outcomes of this approach were compared with manually obtained FTA measurements from the OAI dataset as the ground truth. Based on experiments, the difference in measurement results between the FTA of the OAI and the FTA obtained using our method is quite small, i.e., below 0.81° for the right FTA and below 0.77° for the left FTA with minimal average errors. This result indicates that this method is clinically suitable for semi-automatic measurement of the FTA.