Abstract
In clinical practice, CT scans are frequently employed as the primary imaging modality for detecting prevalent tumors arising from the abdominal organs. Hence, the accomplishment of simultaneous organ segmentation and pan-cancer segmentation in abdominal CT scans holds significant importance in decreasing the workload of clinical practitioners. To maximize the utilization of partially labeled and unlabeled data, a iterative training strategy through a semi-supervised approach based on pseudo labels is employed in this work. Furthermore, to reduce parameter size of model and increase efficiency of GPU utilization, the proposed method is built upon the pocket U-Net architecture. The methodology involves a cascaded network consisting of two parts: initially, a segmentation network trained on labeled data refines the low-resolution pocket U-Net to reduce image dimensions; subsequently, the high-resolution pocket U-Net conducts intricate segmentation to precisely delineate organ and tumor regions. As demonstrated by the evaluation outcomes on the FLARE 2023 validation dataset, the proposed method achieves an average dice similarity coefficient (DSC) of 88.94% for organs and 15.92% for tumors, along with normalized surface dice (NSD) values of 93.31% for organs and 0.0816% for tumors, with minimal parameter size. Furthermore, the average inference time is 82.61 s, with an average maximum GPU memory usage of 3560M. Codes are available at https://github.com/wt812549723/FLARE2023_solution.
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Acknowledgements
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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Wang, T., Zhang, X., Xiong, W., Zhou, S., Zhang, X. (2024). Semi-Supervised Learning Based Cascaded Pocket U-Net for Organ and Pan-Cancer Segmentation in Abdomen CT. 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_13
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