Medical image segmentation method based on multi-scale feature fusion

Abstract

Provided is a medical image segmentation method based on multi-scale feature fusion. The method includes acquiring medical image data of a same type, and sketching a region of interest (ROI); preprocessing sketched image data; constructing a multi-scale feature extraction module, a multi-scale feature fusion module, and an encoder-decoder deep learning network model; performing 5-fold cross-validation on the network model; and evaluating a medical image segmentation result output by the model with an evaluation index including a dice, an accuracy, a precision and a recall. This application extracts multi-scale features comprehensively from an encoder, a decoder, and connection between the encoder and the decoder in the network, and effectively learns multi-scale information in the image. The multi-scale features are fused by an interactive module, so this application relieves feature conformity caused by direct multi-scale fusion, thereby improving performance of the medical image segmentation.

Dependencies are inferred from the claim text.