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

arXiv: https://arxiv.org/abs/2010.01663 · HTML: https://mlsanity.com/paper/2010.01663/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [On-the-Fly Test-time Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2203.05574/) (2022)
- [TBraTS: Trusted Brain Tumor Segmentation](https://mlsanity.com/paper/2206.09309/) (2022)
- [Over-and-Under Complete Convolutional RNN for MRI Reconstruction](https://mlsanity.com/paper/2106.08886/) (2021)
- [Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images](https://mlsanity.com/paper/2206.06665/) (2022)
- [Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction](https://mlsanity.com/paper/2107.02630/) (2021)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Fine-Context Shadow Detection using Shadow Removal](https://mlsanity.com/paper/2109.09609/) (2021)
- [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://mlsanity.com/paper/2201.12785/) (2022)
- [How precise are performance estimates for typical medical image segmentation tasks?](https://mlsanity.com/paper/2210.14677/) (2022)
- [TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2204.12185/) (2022)
- [Masked Supervised Learning for Semantic Segmentation](https://mlsanity.com/paper/2210.00923/) (2022)
- [Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging](https://mlsanity.com/paper/2412.03192/) (2024)
- [Category Guided Attention Network for Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/2203.15383/) (2022)
- [Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture](https://mlsanity.com/paper/2207.04721/) (2022)
- [POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation](https://mlsanity.com/paper/2401.14285/) (2024)
- [Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition](https://mlsanity.com/paper/2409.09216/) (2024)
- [On the Importance of Feature Representation for Flood Mapping using Classical Machine Learning Approaches](https://mlsanity.com/paper/2303.00691/) (2023)
- [LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion](https://mlsanity.com/paper/2305.00837/) (2023)
- [T-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentation](https://mlsanity.com/paper/2507.19590/) (2025)
- [DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images](https://mlsanity.com/paper/2211.14235/) (2022)
- [Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation](https://mlsanity.com/paper/2307.12180/) (2023)
- [Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation](https://mlsanity.com/paper/2404.01127/) (2024)
- [DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation](https://mlsanity.com/paper/2405.00472/) (2024)
- [Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer](https://mlsanity.com/paper/2407.08470/) (2024)
- [Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training](https://mlsanity.com/paper/2504.01547/) (2025)

## Abstract (arXiv)

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