Polyp segmentation in colonoscopy images using DeepLabV3++ (2407.19327v1)

Abstract: Segmenting polyps in colonoscopy images is essential for the early identification and diagnosis of colorectal cancer, a significant cause of worldwide cancer deaths. Prior deep learning based models such as Attention based variation, UNet variations and Transformer-derived networks have had notable success in capturing intricate features and complex polyp shapes. In this study, we have introduced the DeepLabv3++ model which is an enhanced version of the DeepLabv3+ architecture. It is designed to improve the precision and robustness of polyp segmentation in colonoscopy images. We have utilized The proposed model incorporates diverse separable convolutional layers and attention mechanisms within the MSPP block, enhancing its capacity to capture multi-scale and directional features. Additionally, the redesigned decoder further transforms the extracted features from the encoder into a more meaningful segmentation map. Our model was evaluated on three public datasets (CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG) achieving Dice coefficient scores of 96.20%, 96.54%, and 96.08%, respectively. The experimental analysis shows that DeepLabV3++ outperforms several state-of-the-art models in polyp segmentation tasks. Furthermore, compared to the baseline DeepLabV3+ model, our DeepLabV3++ with its MSPP module and redesigned decoder architecture, significantly reduced segmentation errors (e.g., false positives/negatives) across small, medium, and large polyps. This improvement in polyp delineation is crucial for accurate clinical decision-making in colonoscopy.

• The paper introduces DeepLabV3++, an enhanced segmentation model employing EfficientNetV2S and novel modules to significantly improve polyp boundary detection.
• It leverages a Multi-Scale Pyramid Pooling module and skip connections to capture complex features and maintain spatial details in colonoscopy images.
• Experimental results show Dice coefficients above 96% on multiple datasets, underscoring its potential for early colorectal cancer diagnosis.

Analysis of "Polyp segmentation in colonoscopy images using DeepLabV3++"

The process of polyp segmentation in colonoscopic images is of paramount importance as it directly influences the early detection of colorectal cancer, a significant public health challenge globally due to its high incidence and mortality rates. The paper "Polyp segmentation in colonoscopy images using DeepLabV3++" by Islam et al. introduces an improved model, named DeepLabV3++, which addresses existing limitations in polyp segmentation tasks by leveraging advancements in deep learning architectures.

Methodological Advancements

The authors propose DeepLabV3++, an extension of the well-known DeepLabV3+ architecture, integrating an EfficientNetV2S backbone, a Multi-Scale Pyramid Pooling (MSPP) module, and a Parallel Attention Aggregation Block (PAAB). The EfficientNetV2S backbone provides advanced feature extraction with improved computational efficiency and segmentation accuracy.

The MSPP module advances the traditional Atrous Spatial Pyramid Pooling (ASPP) by using diverse separable convolutions and kernel sizes, facilitating enhanced feature extraction over varying scales of colonoscopic images. It efficiently captures multi-scale and directional features while maintaining computational efficiency. Skip connections within the MSPP enhance gradient flow and retain spatial detail, which is crucial for precise polyp border definition.

The PAAB module effectively manages spatial and channel information by integrating spatial and channel attention mechanisms, achieved through depthwise convolutions and multi-kernel separable operation schemes. This ensures that the model focuses on critical image regions such as intricate polyp boundaries.

Experimental Validation

The DeepLabV3++ model demonstrates robust improvements in boundary delineation by achieving high Dice coefficients of 96.20%, 96.54%, and 96.08% on the CVC-ColonDB, CVC-ClinicDB, and Kvasir-SEG datasets, respectively. These results establish the superiority of DeepLabV3++ over several state-of-the-art models in terms of segmentation precision and reliability, leading to fewer false positives and negatives across varied polyp presentations.

The authors conducted ablation studies that validate the significance of the proposed modifications, with the MSPP and PAAB modules contributing quantitatively to improved performance compared to the baseline DeepLabV3+. The enhancement of polyp delineation is evident from both qualitative and quantitative results, highlighting accurate segmentation even in challenging scenarios typical to clinical settings.

Implications and Future Directions

The improvements presented in DeepLabV3++ have noteworthy implications for clinical practice. By reducing segmentation errors and enhancing feature representation, the model can assist in automated CAD systems, potentially transforming early colorectal cancer diagnostics through timely and precise polyp detection. This can lead to optimized clinical workflows by reducing the need for extensive manual assessment by specialists.

Future research may explore the integration of transformer-based encoder-decoder architectures, potentially capturing more complex dependencies and augmenting the spatial representations even further. Additionally, the exploration of transfer learning with domain-specific data might offer improvements in domain-adaption capabilities of the model, expanding its utility beyond polyp segmentation to other medical imaging applications.

In conclusion, the proposed DeepLabV3++ constitutes a notable advancement in the domain of medical image segmentation, specifically for colonoscopy images, by fostering more accurate polyp delineation and setting a foundation for further research in leveraging deep learning technologies for early cancer detection.