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2 April 2024 Automatic detection of exam contents from a 3D image series
Sandeep Dutta, Yunfeng Li, Maud Bonnard, Jiang Hsieh
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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292619 (2024) https://doi.org/10.1117/12.3006283
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Radiology workflow automation requires knowledge of exam contents in an image series such as anatomy region, injected contrast phase, presence of metals, so that appropriate post-processing steps and analysis can be invoked automatically. This paper investigates the applicability of DL to the task of classifying an entire image series into one of fourteen common exam types. A total of 2300 independent computed tomography (CT) image series, each manually labeled for its exam category by clinical experts, was used to train DL models. An additional 593 series were labeled and used as an independent test set. Each CT image series containing a 3D volume acquisition is converted to a special 2D multiplanar-reconstruction (MPR) image. DL based classifier was trained to classify the image series based on this 2D representation, which could be an AP view, a Lateral view or both. Different convolutional neural network architectures with varying block depths were compared. Global average pooling (GAP) layer was used in the final classification block and the impact of input view was studied. The impact of depth of feature extraction layer, input image type, data augmentation techniques and learning rates were studied. The best single class prediction accuracy achieved was 97%. The top-two classes classification accuracy reached > 99%. This method avoids the cost of inferencing each image in a 3D series but still provides very high classification accuracy.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Sandeep Dutta, Yunfeng Li, Maud Bonnard, and Jiang Hsieh "Automatic detection of exam contents from a 3D image series", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292619 (2 April 2024); https://doi.org/10.1117/12.3006283
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KEYWORDS
Computed tomography
Anatomy
Image classification
3D image processing
Convolutional neural networks
Deep learning
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