Abstract
Generating pixel or voxel-wise annotations of radiological images to train deep learning-based segmentation models is a time consuming and expensive job involving precious time and effort of radiologists. Other challenges include obtaining diverse annotated training data that covers the entire spectrum of potential situations. In this paper, we propose an Active Learning (AL) based segmentation strategy involving a human annotator or “Oracle" to annotate interactively. The deep learning-based segmentation model learns in parallel by training in iterations with the annotated samples. A publicly available MRI dataset of brain tumors (Glioma) is used for the experimental studies. The efficiency of the proposed AL-based segmentation model is demonstrated in terms of annotation time requirement compared with the conventional Passive Learning (PL) based strategies. Experimentally it is also demonstrated that the proposed AL-based segmentation strategy achieves comparable or enhanced segmentation performance with much fewer annotations through quantitative and qualitative evaluations of the segmentation results.
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Banerjee, S., Strand, R. (2023). Deep Active Learning for Glioblastoma Quantification. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13885. Springer, Cham. https://doi.org/10.1007/978-3-031-31435-3_13
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