Version 1
: Received: 5 July 2023 / Approved: 10 July 2023 / Online: 11 July 2023 (10:18:40 CEST)
How to cite:
Chen, Y.; Yu, P.; Lai, Y.; Hsieh, T.; Cheng, D. Breast Cancer Bone Metastasis Lesion Segmentation on Bone Scintigraphy. Preprints2023, 2023070674. https://doi.org/10.20944/preprints202307.0674.v1
Chen, Y.; Yu, P.; Lai, Y.; Hsieh, T.; Cheng, D. Breast Cancer Bone Metastasis Lesion Segmentation on Bone Scintigraphy. Preprints 2023, 2023070674. https://doi.org/10.20944/preprints202307.0674.v1
Chen, Y.; Yu, P.; Lai, Y.; Hsieh, T.; Cheng, D. Breast Cancer Bone Metastasis Lesion Segmentation on Bone Scintigraphy. Preprints2023, 2023070674. https://doi.org/10.20944/preprints202307.0674.v1
APA Style
Chen, Y., Yu, P., Lai, Y., Hsieh, T., & Cheng, D. (2023). Breast Cancer Bone Metastasis Lesion Segmentation on Bone Scintigraphy. Preprints. https://doi.org/10.20944/preprints202307.0674.v1
Chicago/Turabian Style
Chen, Y., Te-Chun Hsieh and Da-Chuan Cheng. 2023 "Breast Cancer Bone Metastasis Lesion Segmentation on Bone Scintigraphy" Preprints. https://doi.org/10.20944/preprints202307.0674.v1
Abstract
Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.
Keywords
bone metastasis segmentation; Double U-Net; pre-train; negative mining; transfer learning; deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
