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.
Following the acquisition of images in CT, a crucial post-processing step involves orienting the volumetric image to align with standard viewing planes, facilitating the assessment of disease extent and other pathologies. However, manual alignment is not only time-consuming but can also pose challenges in achieving consistent standard plane views, particularly for lesser skilled technologists. Existing automated solutions, primarily based on registration techniques, encounter reduced accuracy in cases involving significant rotations, pediatric patients, and instances with pronounced pathological effects. This limitation arises due to their reliance on symmetry. In severe scenarios, registration-based methods can exacerbate image misalignment compared to the original input. To address these concerns, this study introduces a landmark-based automated image alignment method. This method presents three key advantages: robust alignment across diverse data variations, the capability to identify algorithm failures and gracefully terminate, and the ability to align images with different standard planes. The effectiveness of our method is showcased through a comparative evaluation with registration-based approaches. The evaluation employs a test dataset comprising various head cases across different age groups, reaffirming the effectiveness of our proposed method.
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