Yu, P.-N.; Lai, Y.-C.; Chen, Y.-Y.; Cheng, D.-C. Skeleton Segmentation on Bone Scintigraphy for BSI Computation. Diagnostics2023, 13, 2302.
Yu, P.-N.; Lai, Y.-C.; Chen, Y.-Y.; Cheng, D.-C. Skeleton Segmentation on Bone Scintigraphy for BSI Computation. Diagnostics 2023, 13, 2302.
Yu, P.-N.; Lai, Y.-C.; Chen, Y.-Y.; Cheng, D.-C. Skeleton Segmentation on Bone Scintigraphy for BSI Computation. Diagnostics2023, 13, 2302.
Yu, P.-N.; Lai, Y.-C.; Chen, Y.-Y.; Cheng, D.-C. Skeleton Segmentation on Bone Scintigraphy for BSI Computation. Diagnostics 2023, 13, 2302.
Abstract
Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.
Keywords
Mask R-CNN; Double U-Net; Deeplabv3 +; bone segmentation; bone scintigraphy
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.
