TransRUPNet for Improved Polyp Segmentation.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Colorectal cancer is a leading global cause of mortality.
• Early detection and removal of precancerous polyps are crucial for preventing cancer progression.
• Accurate polyp segmentation is vital for effective colon cancer screening.

Purpose of the Study:

• To develop an advanced deep learning architecture for automatic and real-time polyp segmentation.
• To improve the accuracy and efficiency of polyp detection in colonoscopy images.
• To evaluate the generalizability of the proposed method on out-of-distribution datasets.

Main Methods:

• Development of a Transformer-based Residual Upsampling Network (TransRUPNet), an encoder-decoder architecture.
• Implementation with three encoder and decoder blocks and additional upsampling blocks.
• Evaluation on in-distribution (PolypGen) and out-of-distribution polyp datasets at 256x256 image size.

Main Results:

• Achieved a real-time operation speed of 47.07 frames per second.
• Obtained an average mean Dice coefficient of 0.7786 and mean Intersection over Union of 0.7210 on out-of-distribution datasets.
• Demonstrated high accuracy for in-distribution datasets and significant performance improvement on out-of-distribution data compared to existing methods.

Conclusions:

• TransRUPNet provides real-time feedback with high accuracy for polyp segmentation.
• The model exhibits strong generalizability, outperforming existing methods on diverse datasets.
• The developed deep learning approach shows promise for enhancing early colorectal cancer detection.