Real Time Multi Organ Classification on Computed Tomography Images
This work addresses the need for efficient organ classification in medical imaging for clinical automation, though it is incremental as it builds on existing classifier and segmentation approaches.
The paper tackled the problem of organ identification in CT images by proposing a classifier-based method that uses large context and sparse sampling to achieve real-time performance, showing faster runtime than existing segmentation techniques.
Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by using a large context size with a sparse data sampling strategy. Although our method operates as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.