A method and system that can be used to adapt the markings of deep learning systems used for obtaining anatomical images for medical purposes in accordance with the movement, change in the image and changes in processing speed
AssigneeSmart Alfa Teknoloji Sanayi Ve Ticaret Anonim Sirketi
InventorsAhmet Sahin YENER, Ayhan OKUYAN, Ömer Ferhad SARIOGLU, Utku KAYA
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Abstract
The present invention relates to the method and system developed to adapt the markings of deep learning systems that mark the images created with methods (especially ultrasonographic or elastographic methods) used for obtaining anatomical images for medical purposes in accordance with the movement, change and changes in processing speed in the image and to increase the clinical stability of deep learning system markings.
Claims10
An anatomical imaging system for medical purposes that enables the generation of an image of the anatomical object and the surrounding tissue and can transfer this image to the said processor, comprising a least one application that can take an image of at least one anatomical structure and surrounding tissue, that includes convolutional neural networks to automatically detect the anatomical object and surrounding tissue, that provides automatic localization and segmentation of the anatomical structures that appear on the image and the surrounding tissue of the image by using convolutional neural networks, that can automatically mark the anatomical structure and surrounding tissue in the generated image and present it to the user as an image;
- at least one processor on which the said application can run;
- a display unit to present the created image to the user;
- characterized in that it comprises a classical imaging or deep learning-based motion tracking module that processes the images consecutively in the flowing sequences to prevent errors such as sudden changes in pure predictions and displacement of the structures by providing a continuous average of the neural network responses.
An anatomical imaging system for medical purposes, comprising the process steps of Providing at least one application that can take an image of at least one anatomical structure and surrounding tissue, that includes convolutional neural networks to automatically detect the anatomical object and surrounding tissue, that provides automatic localization and segmentation of the anatomical structures that appear on the single image and the surrounding tissue of the image by using convolutional neural networks, that can automatically mark the anatomical structure and surrounding tissue in the generated image and present it to the user as an image;
- providing at least one processor on which the said application can run;
- developing and training a deep learning network comprising one or more convolutional neural networks to automatically detect the anatomical object and surrounding tissue;
- automatically positioning and segmenting the anatomical object and surrounding tissue of the image through the neural network;
- automatically labeling the anatomical object and surrounding tissue in the image;
- displaying the labeled image to a user;
- characterized in that it comprises the process steps of converting the output of the classical vision or deep learning based motion tracking module, which extracts the change in the image and the mobility in the image during the sequence by sequentially processing the images in sequences taken from the flowing images, into a recall factor (RF) according to the instantaneous processing speed and creating the visualization from the response processed with the recall factor
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Dependencies are inferred from the claim text.