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

TL;DR

KiU-Net introduces an overcomplete convolutional architecture combining high-resolution detail capture with high-level feature learning, significantly improving small structure and boundary segmentation across various medical imaging modalities.

Contribution

The paper proposes KiU-Net, a novel overcomplete convolutional architecture with dual branches for enhanced biomedical image and volumetric segmentation, outperforming existing methods.

Findings

Outperforms recent segmentation methods across five diverse datasets.

Achieves better accuracy with fewer parameters and faster convergence.

Extensions with residual and dense blocks further improve performance.

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