The 'joint assignment' is a creative bachelor education project for Biomedical Engineering (BME) in Shanghai University (SHU), China. The objective of this project is to improve students' capabilities in design thinking and teamwork through practices in the process of the design and development of complex medical product. As the first step, a pilot project 'design and development of intelligent nursing bed' was set up in May 2015. This paper describes details of how project organization and management, various teaching methods and scientific evaluation approaches were achieved in this pilot project. For example, a method containing one main line and four branches is taken to manage the project and 'prototyping model' was used as the main research approach. As a result a multi-win situation was achieved. The results showed, firstly, 62 bachelor students including 16 BME students were well trained. They improved themselves in use of practical tools, communication skills and scientific writing; Secondly, commercial companies received a nice product design on intelligent nursing bed, and have been working on industrializing it; Thirdly, the university and associated schools obtained an excellent practical education experience to supplement traditional class education; Fourthly and most importantly, requirements from end-users will be met. The results also showed that the 'joint assignment' task could become a significant component in BME bachelor education.

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Accurate segmentation of 11C-Pittsburgh compound B Positron Emission Tomography (11C-PiB PET) images may assist physicians in diagnosing Alzheimer's disease (AD). However, the weak boundaries of high 11C-PiB concentration regions and the heterogeneous distribution of tracers often bring difficulties for segmentation algorithms. This paper aims at investigating the performance of Level Set Method (LSM)-based algorithms in PiB PET images segmentation. We thereby propose a T-L algorithm that uses the mean-thresholding algorithm (T algorithm) to generate the initial contours for the LSM-based algorithm. Priori structure knowledge, which is derived from the Anatomical Automatic Labeling (AAL) model in corresponding Magnetic Resonance Imaging (MRI) images, is incorporated in the T-algorithm. Seventy-four 11C-PiB PET images were used to evaluate the effectiveness and efficiency of the algorithm. The experiment results indicate that, with respect to the dice similarity coefficient, the Hausdorff distance, and the coefficient of variation values, the proposed T-L algorithm (Dice: 94.8%±2.3%, COV: 0.02; HD:11±11 pixel, COV: 1; computing time: 7.39±0.16s) outperforms both the T-algorithm (Dice: 94.%±2.7%, COV: 0.03; HD: 18±18 pixel, COV: 1; computing time: 67.06±0.14s) and the manual assisted LSM-based algorithm (M-L algorithm) (Dice: 93.4%±3.8%, COV: 0.04; HD: 14±13 pixel, COV: 0.9; computing time: 0.33±0.02s). Furthermore, it is also able to save the time and effort of physicians. @en