Endoscopic Image Registration via Neural Network Motion Estimation
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Solution Overview
Problem
The correlation of reflectance and fluorescence images in endoscopic procedures is challenging due to differences in shape, zooming, resolution, contrast, and noise, and the movable nature of the babyscope within the motherscope leads to misalignments, complicating the identification and treatment of targets like tumors.
Innovation Solution
A method involving the registration of images from different endoscopic units using a computing device to estimate and correct for the independent motions of each probe, allowing for the alignment of fluorescence and reflectance images, which involves a neural network for automatic registration and a software system to overlay and display the aligned images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a babyscope is inserted into the working channel of a motherscope for fluorescence imaging, then cost effectiveness and functionality are improved, but image alignment and correlation between the two scopes deteriorate due to unknown movements and misalignments
Solution Approach 1:
A computing device is introduced as an intermediary between the two endoscopic units to receive image data from both scopes, perform image registration to correct misalignments, and generate aligned composite images. This mediator resolves the alignment issue caused by the movable babyscope while preserving the functional benefits of the dual-scope configuration.
2Ease of operation
If the babyscope is made movable within the working channel for flexibility, then ease of operation is improved, but image correlation and alignment deteriorate due to reciprocal rotations and translations
Solution Approach 1:
The system continuously receives image data from both scopes in real-time, processes the alignment information through image registration algorithms, and provides corrected aligned images as feedback. This feedback mechanism dynamically compensates for the movements of the flexible babyscope, maintaining image correlation despite operational flexibility.
3Adaptability or versatility
If different imaging techniques (reflectance and fluorescence) are used simultaneously, then diagnostic capability is improved, but image comparison and correlation become challenging due to differences in shape, zooming, resolution, contrast, and noise
Solution Approach 1:
The image registration process transforms and adjusts the image parameters (position, orientation, scale, and resolution) of one or both images to match the other. By dynamically changing these parameters through computational processing, the system enables accurate correlation between reflectance and fluorescence images despite their inherently different characteristics.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach facilitates accurate alignment and combination of fluorescence and reflectance images, enhancing the precision of medical procedures by correcting for relative movements and differences in image characteristics, thereby improving the visualization of targets during endoscopic examinations.
Implementation Method 1
each motion being estimated according to an estimation set of a plurality of images corresponding to the current image
Data Source
AI summary
A solution is proposed for imaging a body-part (103) of a patient (106) with an endoscopic system (100). A corresponding method (500) comprises acquiring (509-515) two sequences of images with different probes (127n,127b), of corresponding endoscopic units (115m,115b), that are movable therebetween. Corresponding images of each pair in the two sequences are registered (518-584), for example, according to corresponding motions of their probes (127n,127b) that are estimated independently each according to the corresponding images. A method (800) is also proposed for training a neural network (439) that may be used to register the images. Corresponding computer programs (400;700) and computer program products for operating the endoscopic system (100) and for training the neural network (439) are proposed. Moreover, corresponding endoscopic system (100) for imaging the body-part (103), endoscopic equipment (115b) comprising one of the endoscopic units (115m,115b), computing device (242b) for operating the endoscopic system (100) and computing system (600) for training the neural network (439) are proposed. A surgical method, a diagnostic method and a therapeutic method based on the same solution are further proposed.