Abstract
In this paper, there are 2 head computed tomography (CT) scan datasets: one with vision related organs pixel annotations and the other with auditory related organs pixel annotations. We aim to train a single neural network for vision related organs and auditory related organs segmentation at the same time with these 2 partially annotated datasets. An idea generating from co-operative training method will be applied to complete the lacking annotations of each dataset. However, it is not a proper way to treat the predicted annotations as the real annotations from professional doctors. To address this error, a semi-supervised method is chosen. Compared to the baseline method, our training pipeline is able to complete 2 segmentation tasks within only one model, and we have proved that it outperforms the baseline method. To some extent, using partially annotated medical image datasets can help to solve the problem that the scarce source of professionally annotated medical image data. What’s more, the proposed method will achieve better performance.
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Xuzidui, Guantian, Heyonghong (2020). Fusion Segmentation of Head Medical Image with Partially Annotated Data. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_19
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DOI: https://doi.org/10.1007/978-981-15-5199-4_19
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