#NORMAL XRAY HIP 14 YEARS OLD MANUAL#

Some prominent datasets of human body radiographs are compared with the FracAtlas dataset 19 in Table 1. This dataset is also not suitable for bone fracture study as it only contains samples of chest diseases. ChestX-ray14 is a dataset for radio graphs containing 112,000 CXR scans. Likewise, the VinDr group has other datasets VinDr-Mammo 17, VinDr-SpineXR 14 and PediCXR 18 which are not suitable for fracture study for similar reasons. Though this is a good dataset for identifying chest diseases, it’s not suitable for bone fracture identification. This dataset contains samples for 28 different types of chest diseases and abnormalities.

#NORMAL XRAY HIP 14 YEARS OLD MANUAL#

VinDr-CXR is also a recently published dataset, which contains 18,000 images of chest X-rays (CXR) with manual annotation for localization. It also covers only knee joint fractures. This dataset is no longer available in the public domain due to lacking of funding and closeout. MOST is a dataset that contains 4,446 X-ray and MRI scans labeled by the Kellgren–Lawrence (KL) grading system 16 having five classes from grade-0 to grade-4 with increasing severity from one to the next. This dataset is very small and inadequate for machine-learning activities. IIEST is a small dataset of 2D X-rays containing 217 images, of which 49 are healthy, 99 are fractured and 69 are cancerous bone scans. Though this is a suitably large dataset, it covers only wrist fractures, omitting the rest of the parts of the human body. GRAZPEDWRI-DX is a recently released dataset, containing 20,327 scans with annotation for localization collected from 6,091 patients. However, the dataset has issues such as unorganized annotation and falsely labeled images, as well as some spam images. These images include X-rays, real images, Magnetic resonance imaging (MRI), Computed tomography (CT) scans, and ultrasound imaging. MedPix is an online database of 2D and 3D medical scans of various diseases that can be filtered by the keyword ‘fracture’ resulting in 954 images. Each image is labeled as ‘Normal’ or ‘Abnormal’ which makes it suitable for classification tasks, however, it lacks proper annotation for localization and segmentation. Among these datasets, MURA is a collection of 2D muscular skeletal radiographs with 40,561 images from different regions such as the elbow, finger, forearm, hand, humerus, shoulder, and wrist 10. Some of the well-known X-Ray datasets that are publicly available and that focus on anomalies include MURA 10, MedPix 11, GRAZPEDWRI-DX 12, IIEST 1, MOST 13, VinDr-CXR 2, VinDr-SpineXR 14 and ChestX-ray14 15. To sum it up the creation of such datasets is costly and time-consuming 2, 9. Due to the sensitive nature of medical data, it is also very hard to make the acquired data available for public use. The annotation process can be very costly as it requires the involvement of multiple physicians and radiologists for consensus to remove bias and human errors. It is difficult to collect such data from hospitals and diagnostic centers.

Training such models requires large and well-annotated datasets 6, 7, 8.

Models like convolutional neural networks (CNN) 3, You only look once (YOLO) 4 and U-NET 5 can achieve expert-like performance in detecting anomalies from X-Ray scans. Moreover, recently different fields of medical science have seen rapid development of automation processes in diagnosis leveraging large datasets and advanced machine learning algorithms 1, 2.

There has been a surge in demand for computer-aided diagnosis (CAD) systems in recent decades. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. There are 717 images with 922 instances of fractures. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. The development of algorithms demands large datasets with proper annotations. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures.

Normal xray hip 14 years old