Neurofibroma segmentation method combined with space guidance

Abstract

The invention discloses a neural fibroma segmentation method combined with space guidance, and the method comprises the steps: taking nnU-Net as a trunk network, adding a space guidance branch, integrating user interaction information into a network such that the network is better segmented through user interaction on the basis of automatic segmentation; firstly, subjecting an original image to data preprocessing, and then guiding the image to be transmitted into a network with a certain probability according to label calculation space during training; during reasoning, making automatic segmentation, then enabling a user to click false positive and false negative areas to generate a guide label, generating space guidance according to the label, and transmitting the guide label and a test sample into a network together for prediction and cyclic reasoning until the user is satisfied. The deep neural network and the space guidance are combined, automatic segmentation can be completed, user guidance can also be accepted to correct segmentation, and a good segmentation result is obtained on the neurofibroma.

Description

technical field [0001] The invention relates to the field of image processing and deep learning, in particular to the construction of a semi-automatic tumor segmentation network and a training reasoning method, belonging to the field of medical image analysis based on deep learning. Background technique [0002] Neurofibromatosis is an autosomal dominant genetic disease. The main symptoms are café-au-lait spots on the skin and multiple neurofibromas around the skin. , myeloma, optic nerve glioma, etc., if not treated in time, will worsen and cause serious complications. Based on MRI and other medical images, accurate analysis of tumor volume, shape and other information can assist doctors in formulating treatment plans. Traditionally, medical imaging requires radiologists to label organs and tumor regions one by one, which is very time-consuming, and different doctors have different discrimination criteria. Therefore, computer-aided tumor segmentation becomes a strong need...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of automatic and semi-automatic segmentation methods in neurofibroma segmentation, such as difficulty, low efficiency, and low accuracy, the present invention proposes a new deep interactive network model based on the nnU-Net framework, combined with spatial guidance, through Simulated interactive training can not only automatically give segmentation results, but also accept user's sketches to correct the output results

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