Performance Analysis of Dilated One-to-Many U-Net Model for Medical Image Segmentation

Medical image processing applications typically demand highly accurate image segmentation. However, existing segmentation approaches exhibit performance degradation when faced with diverse medical imaging modalities and varied segmentation target sizes. In this paper, we propose and evaluate a dilated One-to-Many U-Net deep learning model that addresses these challenges. The proposed model comprises of four rows of encoder-decoder modules, with each module consisting of three trainable blocks with different layers. The last three rows of the U-Net are extended versions of the three blocks in the first row, with the encoder-decoder blocks connected through the skip connections to the previous rows. The outputs of the last blocks from the last three rows in the decoder are concatenated, and finally, a dilation network is employed to improve the small target segmentation in different medical images. Two datasets have been used for the evaluation: the HC18 grand challenge ultrasound dataset for fetal head segmentation and the Multi-site MRI dataset, including the BIDMC and HK sites, for prostate segmentation in MRI images. The proposed approach achieved Dice and Jaccard coefficients of 96.54% and 93.93%, respectively, for the HC18 grand challenge dataset, 96.76% and 93.97% for the BIDMC site dataset, and 92.58% and 86.96% for the HK site dataset. Statistical analyses showed that the proposed model outperformed several other U-Net-based models.