An HMM-based recognition framework for endovascular manipulations.

Robotic surgical systems are becoming increasingly popular for the treatment of cardiovascular diseases. However, most of them have been designed without considering techniques and skills of natural surgical manipulations, which are key factors to clinical success of percutaneous coronary intervention. This paper proposes an HMM-based framework to recognize six typical endovascular manipulations for surgical skill analysis. A simulative surgical platform is built for endovascular manipulations assessed by five subjects (1 expert and 4 novices). The performances of the proposed framework are evaluated by three experimental schemes with the optimal model parameters. The results show that endovascular manipulations are recognized with high accuracy and reliable performance. Furthermore, the acceptable results can also be applied to the design of next generation vascular interventional robots.

Guide-wire detecting using a modified cascade classifier in interventional radiology.

Endovascular surgery is becoming a widespread procedure to treat cardiovascular diseases (CVDs) such as abdominal aortic aneurysm and peripheral artery disease. The guide-wire is a crucial surgical instrument inserted into vessels to offer guidance to physicians during the surgery. There are some approaches for tracking the guide-wire, most algorithms consist of two phases, namely, the initialization phase and the tracking phase. In the initialization phase, most algorithms use B-splines for modeling the guide-wire which requires manually annotated data. In the tracking phase, the guide-wire motion is non-linearity because it is deforming and changing its shape and size as a result of patients' respiration, some algorithms decompose the non-linearity motion into rigid motion and non-rigid motion, while the computational complexity is high especially for the non-rigid motion. This paper mainly presents an approach to detect the guide-wire. The algorithm has two main advantages. First, without modeling the guide-wire, this approach uses a cascade classifier which can detect the guide-wire under arbitrary motion automatically. Second, by taking the guide-wire motion direction into consideration, the detection accuracy improves significantly. The presented work has been validated on a test set of 349 frames, and the mean tracking accuracy achieves more than 95% which proves the effectiveness of the proposed method.

Fast and stable guidewire simulator for minimally invasive vascular surgery.

In recent years, minimally invasive vascular surgery is widely applied in treatment of cardiovascular diseases, and the manipulation of the guidewire is the essential skill for this surgery. Lots of time and money have to be taken to achieve the skill. In this paper, we present a multithreading guidewire simulator which can help the apprentice to gain the skill and modeling the guidewire is the core technique of the simulator. The guidewire is modeled by a fast and stable method based on the Cosserat theory of elastic rods. The method describes the behavior of the guidewire with the Lagrange equations of motion and it uses the penalty method to maintain constraints. We further propose a simplified solving procedure for the guidewire model. Finally, some experiments are conducted to evaluate the effectiveness of this model.

Detection of tumor cells in peripheral blood of patients with gastric cancer using mRNA of MAGE genes as markers / 中华胃肠外科杂志

<p><b>OBJECTIVE</b>To develop a sensitive and specific RT-PCR assay using the mRNA of MAGE-1 and MAGE-3 genes as specific tumor markers for the detection of the tumor cells in the peripheral blood of patients with gastric cancer.</p><p><b>METHODS</b>Peripheral blood was obtained from 40 patients with gastric cancer and from 20 healthy volunteers. The mRNA of MAGE-1 and MAGE-3 genes in the peripheral blood mononuclear cells (PBMC) was detected by RT-PCR. The expressions of MAGE-1 and MAGE-3 mRNA in the tumor tissues of these gastric cancer patients were also detected by RT-PCR. Meanwhile,CEA expression by nested RT-PCR in PBMC of 40 gastric cancer patients was also detected.</p><p><b>RESULTS</b>Of 40 gastric cancer patients, MAGE-1 and MAGE-3 mRNA were positive in 47.5% (19/40) and 25% (10/40) of PBMC respectively, and in 62.5% (25/40) and 30% (12/40) of gastric cancer tissues respectively. As a whole, in the PBMC of 40 gastric cancer patients, 25 (62.5%) samples were found to express at least one type of MAGE mRNA. In the patients whose tumors did not express MAGE-1 and/or MAGE-3 genes, the corresponding MAGE mRNA was also undetected in their PBMC. There was no expression of MAGE-1 or MAGE-3 gene in the PBMC from the 20 healthy donors. The positive rate of MAGE mRNA in PBMC was closely correlated with the tumor stage and lymph node metastasis (P <0.05). Positive rate of CEA gene expression was 32.5% (13/40) in the PBMC of 40 gastric cancer patients, 29 (72.5%)samples were detected to express at least one type of MAGE gene and CEA gene mRNA.</p><p><b>CONCLUSIONS</b>MAGE-1, MAGE-3 and CEA mRNA are specifically detected with high percentage in the PBMC of gastric cancer patients by RT-PCR. They could be used as specific tumor markers for the detection of the circulating gastric cancer cells, and the detection results may be helpful to evaluate the prognosis of gastric cancer patients.</p>