ABSTRACT
Three-dimensional object modeling is necessary for developing virtual and augmented reality applications. Traditionally, application engineers must manually use art software to edit object shapes or exploit LIDAR to scan physical objects for constructing 3D models. This is very time-consuming and costly work. Fortunately, GPU recently provided a cost-effective solution for massive data computation. With GPU support, many studies have proposed 3D model generators based on different learning architectures, which can automatically convert 2D object pictures into 3D object models with good performance. However, as the demand for model resolution increases, the required computing time and memory space increase as significantly as the parameters of the learning architecture, which seriously degrades the efficiency of 3D model construction and the feasibility of resolution improvement. To resolve this problem, this paper proposes a part-oriented point cloud reconstruction framework called Part2Point. This framework segments the object's parts, reconstructs the point cloud for individual object parts, and combines the part point clouds into the complete object point cloud. Therefore, it can reduce the number of learning network parameters at the exact resolution, effectively minimizing the calculation time cost and the required memory space. Moreover, it can improve the resolution of the reconstructed point cloud so that the reconstructed model can present more details of object parts.
ABSTRACT
OBJECTIVE: Although recent studies have improved understanding of quality of life (QOL) outcomes of breast conserving surgery, few have used longitudinal data for more than two time points, and few have examined predictors of QOL over two years. Additionally, the longitudinal data analyses in such studies rarely apply the appropriate statistical methodology to control for censoring and inter-correlations arising from repeated measures obtained from the same patient pool. This study evaluated an internet-based system for measuring longitudinal changes in QOL and developed a cloud-based system for managing patients after breast conserving surgery. METHODS: This prospective study analyzed 657 breast cancer patients treated at three tertiary academic hospitals. Related hospital personnel such as surgeons and other healthcare professionals were also interviewed to determine the requirements for an effective cloud-based system for surveying QOL in breast cancer patients. All patients completed the SF-36, Quality of Life Questionnaire (QLQ-C30) and its supplementary breast cancer measure (QLQ-BR23) at baseline, 6 months, 1 year, and 2 years postoperatively. The 95% confidence intervals for differences in responsiveness estimates were derived by bootstrap estimation. Scores derived by these instruments were interpreted by generalized estimating equation before and after surgery. RESULTS: All breast cancer surgery patients had significantly improved QLQ-C30 and QLQ-BR23 subscale scores throughout the 2-year follow-up period (p<0.05). During the study period, QOL generally had a negative association with advanced age, high Charlson comorbidity index score, tumor stage III or IV, previous chemotherapy, and long post-operative LOS. Conversely, QOL was positively associated with previous radiotherapy and hormone therapy. Additionally, patients with high scores for preoperative QOL tended to have high scores for QLQ-C30, QLQ-BR23 and SF-36 subscales. Based on the results of usability testing, the five constructs were rated on a Likert scale from 1-7 as follows: system usefulness (5.6±1.8), ease of use (5.6±1.5), information quality (5.4±1.4), interface quality (5.5±1.4), and overall satisfaction (5.5±1.6). CONCLUSIONS: The current trend in clinical medicine is applying therapies and interventions that improve QOL. Therefore, a potentially vast amount of internet-based QOL data is available for use in defining patient populations that may benefit from therapeutic intervention. Additionally, before undergoing breast conserving surgery, patients should be advised that their postoperative QOL depends not only on the success of the surgery, but also on their preoperative functional status.
