MFI-Net: Multi-Level Feature Integration Network With SE-Res2Conv Encoder for Jaw Cyst Segmentation

Accurate segmentation of organs and lesions from medical images holds paramount importance in aiding physicians with diagnosis and monitor diseases. At present, the widespread application of deep-learning in medical image segmentation is primarily attributed to its exceptional feature extraction capability. Nonetheless, due to blurred target boundary, wide range of changes and chaotic background, the segmentation of medical images is still faced with great challenges. To address these issues, we present a multi-level feature integration network (MFI-Net) with SE-Res2Conv encoder for jaw cyst segmentation. Specifically, we replace the original convolution operation with SE-Res2Conv to better maintain model's capacity for extracting features across multiple scales. Then, a novel context extractor module including multi-scale pooling block (MPB) and position attention module (PAM), which aims to generate more discriminative features. Finally, a multi-level feature integration block (MFIB) is implemented within the decoder to efficiently integrate low-level detail features with high-level semantic features. Numerous experiments were conducted on both the original and augmented datasets of jaw cyst to demonstrate the advantages of MFI-Net, with results consistently superior to all competitors. The Dice, IoU and Jaccard values of our method reached 93.06%, 93.47%, 87.06% in the original database and 91.25%, 91.94%, 84.06% in the augmented database. Furthermore, the computational efficiency of MFI-Net is impressive, with a speed of 106.21 FPS and 110.28 FPS at the input size of 3 x 256 x 256 on a NVIDIA RTX6000 graphics card.