Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction.

ABSTRACT: Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Ou

SUBMITTER: Lee H 

PROVIDER: S-EPMC6820559 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature