KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation (2010.01663v2)

Abstract: Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in detecting smaller structures and are unable to segment boundary regions precisely. This issue can be attributed to the increase in receptive field size as we go deeper into the encoder. The extra focus on learning high level features causes the U-Net based approaches to learn less information about low-level features which are crucial for detecting small structures. To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network. We design a new architecture for image segmentation- KiU-Net which has two branches: (1) an overcomplete convolutional network Kite-Net which learns to capture fine details and accurate edges of the input, and (2) U-Net which learns high level features. Furthermore, we also propose KiU-Net 3D which is a 3D convolutional architecture for volumetric segmentation. We perform a detailed study of KiU-Net by performing experiments on five different datasets covering various image modalities like ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), microscopic and fundus images. The proposed method achieves a better performance as compared to all the recent methods with an additional benefit of fewer parameters and faster convergence. Additionally, we also demonstrate that the extensions of KiU-Net based on residual blocks and dense blocks result in further performance improvements. The implementation of KiU-Net can be found here: https://github.com/jeya-maria-jose/KiU-Net-pytorch

• The paper introduces a dual-branch overcomplete architecture that preserves fine anatomical details for enhanced segmentation in biomedical images.
• It combines Kite-Net for low-level detail capture with U-Net for high-level feature extraction, reducing parameters and speeding convergence.
• Empirical results reveal improved Dice scores and reduced Hausdorff distances across multiple imaging modalities, underscoring its practical impact.

Essay: KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

The paper presents KiU-Net, an innovative convolutional architecture designed for biomedical image and volumetric segmentation tasks. Traditional encoder-decoder networks, particularly the U-Net and its variants, have been pivotal in such tasks but exhibit limitations in segmenting small structures and boundary regions accurately. This research identifies that the increased receptive field size, as these models deepen, leads to a focus on high-level features, often at the expense of crucial low-level features required for accurate segmentation of small and detailed structures.

KiU-Net introduces an overcomplete convolutional architecture that projects input images into a higher-dimensional space. This architectural choice restricts the increase of the receptive field size in deeper layers, thereby improving the model’s ability to capture fine details and precise segmentations. The authors propose a dual-branch network—KiU-Net—which combines the overcomplete Kite-Net with a traditional U-Net. Kite-Net captures low-level details effectively, while U-Net extracts high-level features.

In empirical evaluations across varied datasets such as Brain Tumor Segmentation (BraTS), Liver Tumor Segmentation (LiTS), and others spanning different modalities (e.g., MRI, CT, ultrasound), KiU-Net demonstrates superior performance in both segmentation accuracy and computational efficiency. Notably, KiU-Net requires fewer parameters and shows faster convergence than its predecessors, which is a significant advantage considering the typical resource constraints in practical deployments.

The research includes a comprehensive examination of the architecture's performance with extensions like KiU-Net 3D for volumetric segmentation, revealing consistent enhancements in output quality, especially on critical tasks involving sharp edges and small anatomical features. The paper also explores architectural enhancements like Res-KiUNet and Dense-KiUNet, showing further improvements through the addition of residual connections and dense blocks.

Key quantitative results include improvements in Dice scores across datasets and a marked reduction in Hausdorff distance metrics, attesting to the architecture's ability to handle variations in edge definition and structure size. The cross-residual fusion strategy effectively leverages complementary features across branches, contributing to the network's robust learning capability.

Practically, KiU-Net's reduced complexity and parameter efficiency align well with the needs of real-time medical image processing systems, where computational resources are often constrained. Theoretically, the introduction of overcomplete representations opens a new avenue for architectural design in segmentation networks, potentially influencing a range of applications beyond medical imaging.

Future developments in AI and deep learning could see broader applications of concepts demonstrated in KiU-Net. The architecture might inspire adaptations for various scale-invariant or small-object segmentation tasks across other domains. Furthermore, integrating KiU-Net with emerging techniques, such as attention mechanisms and transformer models, could yield further performance gains and application breadth.

By addressing the limitations of traditional encoder-decoder networks and introducing a novel dual-branch approach, KiU-Net establishes itself as an effective strategy for high-precision biomedical segmentation. This work provides a solid foundation for future research exploring overcomplete convolutional architectures in broader contexts.