Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients
Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients
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前列腺癌患者基于深度学习和基于图谱的分割的骨骼分割比较
DOI：
10.1007/s12149-022-01763-3
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发表时间：
2022
影响因子：
2.6
通讯作者：
Terauchi Takashi
中科院分区：
文献类型：
--
作者：
Motegi Kazuki;Miyaji Noriaki;Yamashita Kosuke;Koizumi Mitsuru;Terauchi Takashi
我们的目的是比较基于深度学习（VSBONE BSI）和基于图谱（BONENAVI）的分割准确性，这些分割准确性是为了测量基于骨骼分割的骨扫描指数而开发的。将这些患者分为两组：208例患者注射VSBONE BSI处理的99 mTc-羟基亚甲基二膦酸盐，175例患者注射BONENAVI处理的99 mTc-亚甲基二膦酸盐。三名观察员将以下区域的骨骼分段分类为“匹配”或“不匹配”：颅骨、颈椎、胸椎、腰椎、骨盆、骶骨、肱骨、肋骨、胸骨、锁骨、肩胛骨和股骨。如果两个或多个观察者在同一区域选择了“不匹配”，则定义为分割错误。我们根据每个给药组计算了分割错误率，并评估了“不匹配”区域中疑似骨转移的热点的存在。多变量Logistic回归分析被用来确定分割误差和变量之间的关联，如年龄，摄取时间，总计数，疾病的程度，和伽玛cameras.ResultsThe地区的“不匹配”更常见于长管状骨VSBONE BSI和骨盆和中轴骨骼BONENAVI。VSBONE BSI组49例（23.6%）和BONENAVI组58例（33.1%）出现分割错误。VSBONE BSI在多因素Logistic回归分析中显示，“错配”区域中含有多发骨转移瘤患者疑似骨转移的热点，病变程度越高的患者（优势比= 8.34），分割错误率越高。然而，VSBONE BSI中的分割错误取决于骨转移负荷。使用VSBONE BSI评估多发性骨转移时，我们需要谨慎。
ObjectiveWe aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.MethodsWe retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with99mTc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with99mTc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a “Match” or “Mismatch” in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected “Mismatch” in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.ResultsThe regions of “Mismatch” were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that “Mismatch” regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.ConclusionsVSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.
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基于图谱的骨闪烁扫描图像分割的变形场统计正则化
DOI：
10.1007/978-3-642-04268-3_82
发表时间：
2009
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
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作者：
K. Sjöstrand;M. Ohlsson;L. Edenbrandt
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影响因子：
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DOI：
10.22038/aojnmb.2020.44923.1302
发表时间：
2020-01-01
影响因子：
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作者：
Ichikawa, Hajime;Miwa, Kenta;Onoguchi, Masahisa
通讯作者：
Onoguchi, Masahisa
影响因子：
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作者：
Nakajima K;Nakajima Y;Horikoshi H;Ueno M;Wakabayashi H;Shiga T;Yoshimura M;Ohtake E;Sugawara Y;Matsuyama H;Edenbrandt L
通讯作者：
Edenbrandt L
影响因子：
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作者：
Shibutani, Takayuki;Onoguchi, Masahisa;Nakajima, Kenichi
通讯作者：
Nakajima, Kenichi