Abstract
Accurate and automated abdominal organs and tumors segmentation is of great importance in clinical practice. Due to the high time- and labor-consumption of manual annotating datasets, especially in the highly specialized medical domain, partially annotated datasets and unlabeled datasets are more common in practical applications, compared to fully labeled datasets. CNNs based methods have contributed to the development of medical image segmentation. However, previous CNN models were mostly trained on fully labeled datasets. So it is more vital to develop a method based on partially labeled datasets. In FLARE23, we design a model combining a lightweight nnU-Net and target adaptive loss (TAL) to obtain the segmentation results efficiently and make full use of partially labeled dataset. Our method achieved an average DSC score of 86.40% and 19.41% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 25.34 s and 23018 MB, respectively.
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Acknowledgements
This project was funded by the National Natural Science Foundation of China 82090052. The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2023 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention. We thank all the data owners for making the CT scans publicly available and CodaLab [16] for hosting the challenge platform.
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Liu, T., Zhang, X., Han, M., Zhang, L. (2024). A Lightweight nnU-Net Combined with Target Adaptive Loss for Organs and Tumors Segmentation. In: Ma, J., Wang, B. (eds) Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT. FLARE 2023. Lecture Notes in Computer Science, vol 14544. Springer, Cham. https://doi.org/10.1007/978-3-031-58776-4_14
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