Yi-Ping Hung | PubMed Health

PubMed2006

Validation of a computer-based bronchoscopy simulator developed in Taiwan.

BACKGROUND/PURPOSE: Conventional training in bronchoscopy may increase patient's discomfort and procedure-related morbidity. Computer-based bronchoscopy simulator (CBBS) permits the acquisition and evaluation of the necessary skills through a realistic bronchoscopic experience. This study was conducted to validate the use of a CBBS system developed in Taiwan as a learning and assessment tool. METHODS: Twenty novice bronchoscopists and 10 expert bronchoscopists were enrolled as subjects in this prospective study. The 20 novice bronchoscopists were randomized into two groups, which received conventional bronchoscopic training or CBBS training and then completed a satisfaction survey. Subsequently, the novices who received CBBS training underwent an observational performance trial and the results were compared with those of expert bronchoscopists. All 10 expert bronchoscopists completed a realism survey and observational trial after CBBS performance. RESULTS: The satisfaction survey showed that the CBBS training program significantly increased participants' satisfaction (p = 0.002) and interest in learning (p < 0.001). The realism survey by the 10 expert bronchoscopists indicated that CBBS provides a favorable degree of realism with regard to the mechanical and visual parameters examined. Analysis of the performance results showed that the following parameters were capable of differentiating the participants by level of expertise: total procedure time (p = 0.002), percentage of bronchial segments entered (p = 0.012), percentage of bronchial segments identified (p < 0.001), percentage of repeated bronchial segments entered (p = 0.004), percentage of pathologies identified (p < 0.001), number of times that the bronchoscope tip collided with airway walls (p = 0.013), and number of times oral instruction was needed (p = 0.01). CONCLUSION: CBBS is a valid training method that increases interest in learning and provides a favorable degree of virtual realism. It can also distinguish various levels of competence at actual bronchoscopy and may have a useful role in the bronchoscopic training curriculum.

Bronchoscopy↗

PubMed2006

Comparison between immersion-based and toboggan-based watershed image segmentation.

Watershed segmentation has recently become a popular tool for image segmentation. There are two approaches to implementing watershed segmentation: immersion approach and toboggan simulation. Conceptually, the immersion approach can be viewed as an approach that starts from low altitude to high altitude and the toboggan approach as an approach that starts from high altitude to low altitude. The former seemed to be more popular recently (e.g., Vincent and Soille), but the latter had its own supporters (e.g., Mortensen and Barrett). It was not clear whether the two approaches could lead to exactly the same segmentation result and which approach was more efficient. In this paper, we present two "order-invariant" algorithms for watershed segmentation, one based on the immersion approach and the other on the toboggan approach. By introducing a special RIDGE label to achieve the property of order-invariance, we find that the two conceptually opposite approaches can indeed obtain the same segmentation result. When running on a Pentium-III PC, both of our algorithms require only less than 1/30 s for a 256 x 256 image and 1/5 s for a 512 x 512 image, on average. What is more surprising is that the toboggan algorithm, which is less well known in the computer vision community, turns out to run faster than the immersion algorithm for almost all the test images we have used, especially when the image is large, say, 512 x 512 or larger. This paper also gives some explanation as to why the toboggan algorithm can be more efficient in most cases.

Algorithms↗