Analysis of arterial sub-trees affected by Pulmonary Emboli

©2004 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.535993DOI: 10.1117/12.535993Presented at Medical imaging 2004. Image processing : 16-19 February 2004, San Diego, California, USA.Although Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the U.S., it may also be one of the most preventable. Images acquired from 16-slice Computed Tomography (CT) machines of contrast-injected patients provide sufficient resolution for the localization and analysis of emboli located in segmental and sub-segmental arteries. After a PE is found, it is difficult to assess the local characteristics of the affected arterial tree without automation. We propose a method to compute characteristics of the local arterial tree given the location of a PE. The computed information localizes the portion of the arterial tree that is affected by the embolism. Our method is based on the segmentation of the arteries and veins followed by a localized tree computation at the given site. The method determines bifurcation points and the remaining arterial tree. A preliminary segmentation method is also demonstrated to locally eliminate over-segmentation of the arterial tree. The final result can then be used assess the affected lung volume and arterial supply. Initial tests revealed a good ability to compute local tree characteristics of selected sites

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist

Doctors as servants of patients: Surely we can do better than this

©2004 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: DOI Link: http://dx.doi.org/10.1117/12.532892Presented at Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display,: 15 February 2004, San Diego, California, USA.DOI: 10.1117/12.532892Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the US. The recent introduction of 16-slice Computed Tomography (CT) machines allows the acquisition of very high-resolution datasets. This has made CT a more attractive means for diagnosing PE, especially for previously difficult to identify small subsegmental peripheral emboli. However, the large size of these datasets makes it desirable to have an automated method to help radiologists focus directly on potential candidates that might otherwise be overlooked. We propose a novel method to highlight potential PEs on a 3D representation of the pulmonary arterial tree. First lung vessels are segmented using mathematical morphology techniques. The density values inside the vessels are then used to color the outside of a Shaded Surface Display (SSD) of the vessel tree. As PEs are clots of significantly lower Hounsfield Unit (HU) values than surrounding contrast-enhanced blood, they appear as salient contrasted patches in this 3D rendering. During preliminary testing on 6 datasets 19 PEs out of 22 were detected (sensitivity 86%) with 2 false positives for every true positive (Positive Predictive Value 33%)

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To be presented at SPIE Medical Imaging 2004 Analysis of arterial sub-trees affected by Pulmonary Emboli

Although Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the U.S., it may also be one of the most preventable. Images acquired from 16-slice Computed Tomography (CT) machines of contrast-injected patients provide sufficient resolution for the localization and analysis of emboli located in segmental and sub-segmental arteries. After a PE is found, it is difficult to assess the local characteristics of the affected arterial tree without automation. We propose a method to compute characteristics of the local arterial tree given the location of a PE. The computed information localizes the portion of the arterial tree that is affected by the embolism. Our method is based on the segmentation of the arteries and veins followed by a localized tree computation at the given site. The method determines bifurcation points and the remaining arterial tree. A preliminary segmentation method is also demonstrated to locally eliminate over-segmentation of the arterial tree. The final result can then be used assess the affected lung volume and arterial supply. Initial tests revealed a good ability to compute local tree characteristics of selected sites