METHODS: A dataset consisting of fifty CT scans of patients with manually annotated femoral tumors was created. Forty of them, chosen randomly, were used for training the nnU-Net, while the remaining ten CT scans were used for testing. The deep learning model\'s performance was compared to two experienced radiologists.
RESULTS: The proposed algorithm outperformed the current state-of-the-art solutions, achieving dice similarity scores of 0.67 and 0.68 on the test data when compared to two experienced radiologists, while the dice similarity score for inter-individual variability between the radiologists was 0.73.
CONCLUSIONS: The automatic algorithm may segment lytic femoral tumors in CT scans as accurately as experienced radiologists with similar dice similarity scores. The influence of the realistic tumors inclusion in an autonomous finite element algorithm is presented in (Rachmil et al., \"The Influence of Femoral Lytic Tumors Segmentation on Autonomous Finite Element Analyses\", Clinical Biomechanics, 112, paper 106192, (2024)).
方法:创建了一个数据集,该数据集包括人工注释股骨肿瘤患者的50次CT扫描。其中40个,随机选择,用于训练NNU网络,其余10次CT扫描用于检测。将深度学习模型的性能与两位经验丰富的放射科医生进行了比较。
结果:所提出的算法优于当前最先进的解决方案,与两位经验丰富的放射科医生相比,测试数据的骰子相似性得分分别为0.67和0.68,而放射科医师之间个体间差异的骰子相似性得分为0.73。
结论:自动算法可以像经验丰富的放射科医生一样准确地在CT扫描中分割溶解性股骨肿瘤,并具有相似的骰子相似性评分。在(Rachmil等人。,“股骨溶解性肿瘤分割对自主有限元分析的影响”,临床生物力学,112,论文106192,(2024))。