ResUNet++: An Advanced Architecture for Medical Image Segmentation (1911.07067v1)

Abstract: Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62% with CVC-612 dataset.

• The paper introduces ResUNet++, a novel architecture that significantly improves colorectal polyp segmentation accuracy in medical imaging.
• It employs advanced components such as residual units, squeeze-excitation blocks, ASPP, and attention modules to enhance feature extraction and robustness.
• Empirical results on Kvasir-SEG and CVC-612 datasets demonstrate superior performance with dice coefficients reaching 81.33%, highlighting its clinical potential.

ResUNet++: An Advanced Architecture for Medical Image Segmentation

The paper "ResUNet++: An Advanced Architecture for Medical Image Segmentation" presents a novel deep learning architecture aimed at improving the segmentation accuracy of colorectal polyps in colonoscopic images. Colorectal cancer (CRC) is a significant cause of cancer-related deaths, making the early detection and segmentation of polyps critical for preventing cancer progression. The proposed ResUNet++ architecture extends the established ResUNet framework by incorporating several advanced techniques to boost segmentation performance.

Motivation and Challenges

The primary motivation behind this research is the enhancement of computer-aided detection (CAD) systems that can aid endoscopists in identifying polyps in real-time during colonoscopy sessions. Current CAD systems often fall short due to the variability in polyp appearance and the high costs associated with collecting and annotating medical datasets. Polyp images exhibit a wide range of shapes, sizes, and colors, posing a significant challenge for automated segmentation methods. Additionally, the presence of occlusions and background object similarities further complicates the task. The authors address these challenges by proposing a more sophisticated neural network that leverages recent advances in deep learning.

Proposed Architecture: ResUNet++

ResUNet++ is derived from the ResUNet architecture, itself an extension of U-Net, known for its success in biomedical image segmentation. The key innovations in ResUNet++ include:

1. Residual Units: Utilized for propagating information across layers, thus enabling the construction of deeper networks without degradation issues.
2. Squeeze and Excitation Blocks: These enhance the network's sensitivity to relevant features by modeling channel-wise dependencies, thus improving feature recalibration.
3. Atrous Spatial Pyramidal Pooling (ASPP): It captures multi-scale information by re-sampling features at multiple rates, thereby enlarging the field-of-view of the filters.
4. Attention Units: Incorporated in the decoder path to allow the network to focus on critical regions within the feature maps, enhancing the segmentation accuracy.

The architecture consists of a sequence of encoder and decoder blocks with residual and attention mechanisms interspersed. ASPP acts as a bridge between the encoder and decoder, facilitating the capture of multiple spatial scales.

Experimental Results

The efficacy of ResUNet++ is demonstrated on two publicly available datasets: Kvasir-SEG and CVC-612. The experimental setup includes detailed preprocessing steps like cropping and resizing, along with data augmentation techniques to increase the robustness and generalization capacity of the model.

Kvasir-SEG Dataset Results:

• Dice Coefficient: 81.33%
• Mean Intersection over Union (mIoU): 79.27%
• The ResUNet++ architecture significantly outperforms the baseline U-Net and ResUNet models on the Kvasir-SEG dataset, with notable improvements in both the dice coefficient and mIoU metrics.

CVC-612 Dataset Results:

• Dice Coefficient: 79.55%
• Mean Intersection over Union (mIoU): 79.62%
• ResUNet++ also achieves superior segmentation performance on the CVC-612 dataset. This consistent performance across different datasets highlights the model's robustness.

Implications and Future Work

The improved performance metrics illustrate the potential of ResUNet++ for clinical applications where precise polyp segmentation can assist in better disease management and treatment planning. The implications of this research extend to optimizing CAD systems for real-time applications, ultimately reducing the CRC-related mortality rate. Furthermore, the paper underscores the importance of advanced architectural innovations in enhancing segmentation tasks' accuracy and reliability.

While ResUNet++ shows promising results, future work could focus on expanding the model's applicability to other medical imaging tasks and exploring further optimization techniques to reduce training time and computational cost. Additionally, integrating post-processing steps may yield even more accurate segmentation outputs.

In conclusion, ResUNet++ represents a significant advancement in the field of medical image segmentation, offering a robust, high-performing solution for colorectal polyp detection. Its architecture, enriched with residual, squeeze and excitation, ASPP, and attention units, not only outperforms existing models but also sets a strong baseline for further exploration and development in medical image analysis.