DAAM-Net: A dual-encoder U-Net network with adjacent auxiliary module for pituitary tumor and jaw cyst segmentation

In the diagnosis of various diseases, accurate segmentation of lesions in medical images is crucial. However, when faced with the challenges of blurred edges, noise, and low contrast in images of pituitary tumors and jaw cysts, existing methods do not perform well in solving these problems. To overcome these problems, we present a new approach based on U-Net architecture. Firstly, a more comprehensive semantic information capture is realized by superimposing two U-Net network structures. Following that, the method incorporates atrous spatial pyramid pooling (ASSP) technology to enable adjustable multi-scale feature extraction. Thirdly, an adjacent auxiliary module (AAM) is designed to smoothly fuse features from different levels, effectively suppressing noise interference and reducing feature ambiguity. By testing on the dataset provided by Quzhou People's Hospital, the IoU, Dice, Specificity, and Mcc of the method were 77.46%, 87.18%, 99.64% and 86.97% on the pituitary tumor dataset, and 81.32%, 89.53%, 99.91% and 89.55% on the jaw cyst dataset, respectively. Compared with existing algorithms, our method exhibits superior segmentation performance and is expected to become a more reliable auxiliary tool in the diagnosis of medical diseases.