Adaptive Context Selection for Polyp Segmentation (2301.04799v1)

Abstract: Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and size of polyp. In recent years, state-of-the-art methods have achieved significant breakthroughs in this task with the help of deep convolutional neural networks. However, few algorithms explicitly consider the impact of the size and shape of the polyp and the complex spatial context on the segmentation performance, which results in the algorithms still being powerless for complex samples. In fact, segmentation of polyps of different sizes relies on different local and global contextual information for regional contrast reasoning. To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM). Specifically, LCA modules deliver local context features from encoder layers to decoder layers, enhancing the attention to the hard region which is determined by the prediction map of previous layer. GCM aims to further explore the global context features and send to the decoder layers. ASM is used for adaptive selection and aggregation of context features through channel-wise attention. Our proposed approach is evaluated on the EndoScene and Kvasir-SEG Datasets, and shows outstanding performance compared with other state-of-the-art methods. The code is available at https://github.com/ReaFly/ACSNet.

• The paper presents an encoder-decoder framework that adaptively selects local and global contextual features for enhanced polyp segmentation.
• It employs a trio of modules—Local Context Attention, Global Context Module, and Adaptive Selection Module—to better capture diverse polyp characteristics.
• Experimental results on EndoScene and Kvasir-SEG demonstrate significant improvements in Dice Score, IoU, precision, and recall over state-of-the-art models.

Adaptive Context Selection for Polyp Segmentation

The paper "Adaptive Context Selection for Polyp Segmentation" presents a novel approach to addressing the challenges of polyp segmentation in colonoscopy images by utilizing an adaptive context selection-based encoder-decoder framework. The task of accurately segmenting polyps is critical given its role in the early diagnosis and treatment of colorectal cancer, a malignancy with significant morbidity and mortality rates.

Overview of the Proposed Methodology

This research introduces a framework that emphasizes the distinction between local and global contextual information necessary for polyp segmentation due to the diverse shapes and sizes of polyps. The proposed architecture integrates three key modules: Local Context Attention (LCA), Global Context Module (GCM), and Adaptive Selection Module (ASM).

1. Local Context Attention (LCA) Module:
   • Acts as a spatial attention mechanism designed to highlight challenging regions in the segmentation task.
   • It leverages the prediction map from the previous layer to focus attention on uncertain regions, thereby guiding the current layer to refine these areas.
2. Global Context Module (GCM):
   • Extracts multi-resolution global information, using a pyramid pooling strategy to capture features at varying scales.
   • The inclusion of a non-local operation extends the ability to account for overarching spatial dependencies, crucial for the segmentation of larger polyp regions.
3. Adaptive Selection Module (ASM):
   • Facilitates channel-wise attention for feature recalibration, thereby enabling adaptive fusion of local and global contexts based on the polyp characteristics.
   • This component is essential for dynamically aggregating context-sensitive features to enhance segmentation performance.

Experimental Evaluation

The proposed ACSNet was evaluated on two major datasets, the EndoScene and Kvasir-SEG, which are widely used benchmarks for colonoscopy image analysis. The results indicate that ACSNet consistently outperforms existing state-of-the-art models such as UNet, UNet++, SegNet, and SFANet across several performance metrics including Dice Score, Intersection-over-Union (IoU) for both polyp and background, and overall accuracy. Particularly, the model showed significant improvements in precision and recall, indicating enhanced ability to delineate polyp boundaries accurately.

Implications and Future Directions

The ACSNet represents a significant advancement in leveraging contextual information for medical image segmentation. The modular approach of context selection, specifically the adaptive integration of local and global features, suggests a versatile mechanism adaptable for other segmentation tasks with similar challenges. Future work can explore the extension of this framework to varied medical imaging challenges, incorporating advanced attention mechanisms or hybrid architectures to further improve robustness and accuracy.

The availability of the code promotes reproducibility and invites further refinement by the research community, potentially leading to quicker adoption in clinical practice through integration in computer-aided diagnostic systems. The advancement of polyp segmentation accuracy as demonstrated by this adaptive framework may significantly contribute towards reducing the manual workload and misdiagnosis rates in clinical settings, ultimately benefiting patient outcomes.