The rotational distortion of endoscopic Optical Coherence Tomography (OCT) is caused by friction of optical fiber and motor instabilities. On-line rotational distortion compensation is essential to provide real-time feedback. We proposed a new method that integrates a Convolutional Neural Network based warping parameters prediction algorithm to correct the azimuthal position of each image line. This method solves the problem of drift in iterative processing by an overall shifting parameter predicting nets with a processing time of 145ms/frame and variation reduction of 88.9% for the data obtained in ex-vivo and in-vivo experiments.
Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early stage colorectal cancer that can be performed in teleoperation with a robotized flexible interventional endoscope. However, the tissue elevation step which requires submucosal needle insertion still requires manual operation. In this work we present robotic needle placement using image-guidance that combines white-light camera images to control the alignment of the needle and the OCT catheter. OCT images are used to determine the position of the needle tip during its insertion. This procedure is experimentally tested in an optical phantom that simulates the tissue layers of the colon.
Flexible endoscopes are used in many diagnostic and interventional procedures. Physiological motions may be
very difficult to handle with such device, hence disturbing the physician in completing his task. One way of
dealing with these motions is to have the endoscope following them on its own. To achieve such a goal one needs
to motorize the flexible endoscope and to know accurately the position of the region of interest (target), in order
to control the motors. To this purpose a tracking algorithm is used, which estimates the position of the target in
the images acquired by the camera of the endoscope. But the tracking algorithm needs to be initialized correctly
so as not to lose the target. The difficulty is that targets have many different characteristics and we have no
prior knowledge about them. Besides we want the algorithm to be user friendly, particularly for the physicians,
which means that no parameter has to be tuned even with completely different targets. The proposed algorithm
computes a modified gradient image from first-order moments to obtain smooth edges and reduce the number
of regions found during the next step. A watershed method is used to detect regions. Thanks to the previous
processing of the image, most irrelevant regions will not be detected. Then a merging process is applied which
results in a region corresponding to the target. From the border of this region we find a patch that will be used
to initialize the tracking algorithm. Experimental results are promising.
Usual localization and registration techniques cannot be used for suturing in laparoscopic surgery. The small size of the needle and the interactions with the tissues do not allow to use conventional sensors. Moreover, the possible modifications of the positions of the needle after the needle has been introduced into the abdomen, makes the use of usual vision-based techniques impossible. In this paper, we present methods to obtain the necessary information by using a color endoscopic camera. So as to simplify the detection and reconstruction problems, the needle-holder is modelled as a cylinder and equipped with passive markers, and the needle is colored. Image processing techniques allow to get an elliptical representation
of the image of the needle. From this ellipse and apparent contours of the cylinder, the 3D poses can be obtained. These poses and the needle handling parameters are computed by minimizing the projection error in the images and by using a numerical iterative technique: virtual visual servoing.