FCN-Transformer Feature Fusion for Polyp Segmentation

Abstract

Colonoscopy is widely recognised as the gold standard procedure for the early detection of colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications, namely lesion detection and classification, providing means to improve accuracy and robustness. The manual segmentation of polyps in colonoscopy images is time-consuming. As a result, the use of deep learning (DL) for automation of polyp segmentation has become important. However, DL-based solutions can be vulnerable to overfitting and the resulting inability to generalise to images captured by different colonoscopes. Recent transformer-based architectures for semantic segmentation both achieve higher performance and generalise better than alternatives, however typically predict a segmentation map of $\frac{h}{4}\times \frac{w}{4}$ spatial dimensions for a $h\times w$ input image. To this end, we propose a new architecture for full-size segmentation which leverages the strengths of a transformer in extracting the most important features for segmentation in a primary branch, while compensating for its limitations in full-size prediction with a secondary fully convolutional branch. The resulting features from both branches are then fused for final prediction of a $h\times w$ segmentation map. We demonstrate our method's state-of-the-art performance with respect to the mDice, mIoU, mPrecision, and mRecall metrics, on both the Kvasir-SEG and CVC-ClinicDB dataset benchmarks. Additionally, we train the model on each of these datasets and evaluate on the other to demonstrate its superior generalisation performance.

16 pages, 4 figures

References in corpus (5)

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
• UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation
• HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
• FCN-Transformer Feature Fusion for Polyp Segmentation

Cited by in corpus (3)

• Using DUCK-Net for Polyp Image Segmentation
• FCN-Transformer Feature Fusion for Polyp Segmentation
• Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization