Discrete-MultiResUNet: Segmentation and feature extraction model for knee MR images

Deep learning has shown outstanding efficiency in medical image segmentation. Segmentation of knee tissues is an important task for early diagnosis of rheumatoid arthritis (RA) with selecting variant features. Automated segmentation and feature extraction of knee tissues are desirable for faster and reliable analysis of large datasets and further diagnosis. In this paper a novel architecture called as Discrete-MultiResUNet, which is a combination of discrete wavelet transform (DWT) with MultiResUNet architecture is applied for feature extraction and segmentation, respectively. This hybrid architecture captures more prominent features from the knee magnetic resonance image efficiently with segmenting vital knee tissues. The hybrid model is evaluated on the knee MR dataset demonstrating outperforming performance compared with baseline models. The model achieves excellent segmentation performance accuracy of 96.77% with a dice coefficient of 98%.

Automated summary

The novel architecture called Discrete-MultiResUNet, combining discrete wavelet transform with MultiResUNet, is applied for knee tissue feature extraction and segmentation. This hybrid model efficiently captures significant features from knee MRI images and demonstrates better segmentation performance compared to baseline models, achieving an accuracy of 96.77% and a dice coefficient of 98%.