Abstract：
BACKGROUND: Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element analysis of a patient\'s femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture and the need for a prophylactic fixation. Improving the accuracy of such analyses ruqires an automatic and accurate segmentation of the tumors and their automatic inclusion in the finite element model. We present herein a deep learning algorithm (nnU-Net) to automatically segment lytic tumors within the femur.
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)).
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)).
摘要：
背景：股骨转移性肿瘤在日常活动中可能导致病理性骨折。对患者股骨进行基于CT的有限元分析可帮助整形外科医生就骨折风险和预防性固定的必要性做出明智的决定。提高此类分析的准确性可以自动，准确地分割肿瘤并将其自动包含在有限元模型中。我们在此提出了一种深度学习算法（nnU-Net）来自动分割股骨内的溶解性肿瘤。
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))。
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))。
