A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experiments to evaluate the effectiveness of the proposed segmentation method. In the experiments, a QCT dataset which included 397 QCT subjects was used. For the QCT image of each subject, the ground truth for the proximal femur was delineated by a well-trained scientist. During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models. Results: Visual comparison demonstrated high agreement between the model prediction and ground truth contours of the proximal femur portion of the QCT images. In the entire cohort, the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was obtained when comparing the volumes measured by our model prediction with the ground truth. Conclusion: This method shows a great promise for clinical application to QCT and QCT-based finite element analysis of the proximal femur for evaluating osteoporosis and hip fracture risk

Changes in Femoral Structure and Function Following Anterior Cruciate Ligament Injury and with Aging

The ACL, a ligament connected to the distal femur, has little regenerative capacity. In consequence, surgical intervention is required if a patient hopes to remain active following ACL injury. In addition to the long recovery time and associated morbidities (e.g., osteoarthritis) following surgery, up to 12% of the primary reconstructed ACL grafts will fail within 15 years. Revision reconstructions are inferior to primary ACL reconstructions, thus, understanding the mechanism of failure is critical to mitigating worst-case outcomes. Reasons for revision risk have largely focused on technical errors despite that biological factors may also be a cause. Bone, a biological factor, decreases in mass following ACL injury. However, how bone microstructure changes following injury has remained largely unexplored. It was determined in this study that bone microstructure differs on a patient-by-patient basis undergoing ACL reconstructive surgery. Differences in microarchitecture could not be explained by time from injury to operation (i.e. time of disuse) or activity the patient was participating in at the moment of injury. Thus, differences in bone quality are due to variability present at baseline, in response to injury, and/or activity level following injury. Clinically, these findings are important because we are the first to show that bone quality varies across patient groups, pointing out that microstructure may be an important factor to consider in assessing ACL injury risk and surgical outcomes. The second half of this thesis compared age-related and sex-specific differences in bone microstructure to whole bone strength in the proximal femur with the long term goal of improving diagnostic methods to assess osteoporotic hip fracture risk. Hip fragility fractures are costly, associated with a severe decrease in the quality of life, and nearly half of patients (>65 years) who suffer a hip fracture never regain normal function. Unfortunately, approximately fifty percent of patients that experience a hip fracture receive no prophylactic treatment prior to fragility fracture because they are not diagnosed as osteoporotic using current clinical diagnostic methods. Both bone mass and microstructure change with age and the progression of osteoporosis. However, technical limitations have made it difficult to measure fracture risk from a biomechanical perspective - relating proximal femur bone strength and microstructure in synergy. The second study determined that the magnitude of sex-specific differences in bone strength was greater than age-related strength loss endured throughout life. Further, there was no sex-specific difference in the rate of loss observed herein. Clinically, these findings demonstrate that if females could maximize bone quality early in life, they may be able to maintain the structural strength later on, even with bone loss, to mitigate fragility fractures altogether. Further, mechanical variables (i.e., stiffness and post-yield-displacement) and demographic data (i.e., age and sex) could not adequately explain variability in whole bone strength. Microstructural analysis in the femoral neck improved our ability to predict whole bone strength but demonstrated that sub-regional microstructural detail only modestly improved strength predictability in comparison to average measures across the femoral neck. Despite this, we found that increased levels of micro-architectural detail are needed to identify sex-specific differences in whole bone strength. Clinically, these findings demonstrate that regional analysis may be useful for identifying those at greatest risk of fracture earlier in life and in a sex-specific manner.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155081/1/pattondm_1.pd

Brain and Human Body Modelling 2021

This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book