Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation

An evaluation of a checklist in Musculoskeletal (MSK) radiographic image interpretation when using Artificial Intelligence (AI)

Background: AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. Method: A checklist, to be used in image interpretation with AI assistance, was developed. Participants interpreted 20 examinations with AI assistance and then re- interpreted the 20 examinations with AI and a checklist. The MSK images were presented to radiographers as patient examinations to replicate the image interpretation task in clinical practice. Image diagnosis and confidence levels on the diagnosis provided were collected following each interpretation. The participant perception of the use of the checklist was investigated via a questionnaire.Results: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. The impact of the use of a checklist in image interpretation with AI will be evaluated. Changes in accuracy and confidence will be investigated and results will be presented. Participant feedback will be analysed to determine perceptions and impact of the checklist also. Conclusion: A novel checklist has been developed to aid the interpretation of images when using AI. The checklist has been tested for its use in assisting radiographers in MSK image interpretation when using AI.<br/

Studies on neutron diffraction and X-ray radiography for material inspection

Among the different probes to study the structures of the bio and structural materials, X-ray and neutron are widely used because of their distinctive usefulness in investigating different structures. X-ray radiography and neutron diffraction are two widely known non-destructive techniques for material inspection. Here we demonstrate the design of neutron diffractometer with low power source and analyze the digital image produced by the X-ray radiography instead of neutron diffraction because of the availability of the data. Neutron diffraction is a powerful tool for understanding the behavior of crystal structures and phase behaviors of materials. While neutron diffraction capabilities continue to explore new frontiers of materials science, such capabilities currently exist in limited places, which require high neutron flux. The study seeks to design a low-resolution neutron diffraction system that can be installed on low power reactors (e.g. 250 kW thermal power). The performance of the diffractometer is estimated using Monte-Carlo ray-tracing simulations with McStas with an application in material science. Both monochromatic and polychromatic configurations are considered in order to maximize the net diffracted neutron flux at the detectors with reasonable resolution. On the other hand, considering X-ray radiography as a structure inspecting technique, analysis of dental X-ray panorama is performed for the detection of oral lesions. A novel automatic computer-aided method to identify dental lesions from dental X-ray is presented. Morphological operations, intensity profile analysis, automated seed point selection, region growing, feature extraction and neural network application are carried out to perform the job. Results show that the performance of the proposed method surpasses existing automated methods utilizing dental X-rays --Abstract, page iii

Living with the Semantic Gap: Experiences and remedies in the context of medical imaging

Semantic annotation of images is a key concern for the newly emerged applications of semantic multimedia. Machine processable descriptions of images make it possible to automate a variety of tasks from search and discovery to composition and collage of image data bases. However, the ever occurring problem of the semantic gap between the low level descriptors and the high level interpretation of an image poses new challenges and needs to be addressed before the full potential of semantic multimedia can be realised. We explore the possibilities and lessons learnt with applied semantic multimedia from our engagement with medical imaging where we deployed ontologies and a novel distributed architecture to provide semantic annotation, decision support and methods for tackling the semantic gap problem