# Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

arXiv: https://arxiv.org/abs/1911.00140 · HTML: https://mlsanity.com/paper/1911.00140/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 28 of 28)

- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis](https://mlsanity.com/paper/2112.01148/) (2021)
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- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting](https://mlsanity.com/paper/2312.04964/) (2023)
- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)
- [PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images](https://mlsanity.com/paper/2103.00274/) (2021)
- [Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting](https://mlsanity.com/paper/2108.00911/) (2021)
- [Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network](https://mlsanity.com/paper/2307.08268/) (2023)
- [MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy](https://mlsanity.com/paper/2412.19464/) (2024)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)
- [Diversity-Promoting Ensemble for Medical Image Segmentation](https://mlsanity.com/paper/2210.12388/) (2022)
- [HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation](https://mlsanity.com/paper/2304.04614/) (2023)
- [Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images](https://mlsanity.com/paper/2103.13495/) (2021)
- [Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2409.05024/) (2024)
- [Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers](https://mlsanity.com/paper/2201.10981/) (2022)
- [Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning](https://mlsanity.com/paper/2404.04202/) (2024)
- [ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation](https://mlsanity.com/paper/2412.19713/) (2024)
- [Adversarial Multi-Task Learning for Liver Tumor Segmentation, Dynamic Enhancement Regression, and Classification](https://mlsanity.com/paper/2511.20793/) (2025)
- [RA V-Net: Deep learning network for automated liver segmentation](https://mlsanity.com/paper/2112.08232/) (2021)
- [High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss](https://mlsanity.com/paper/2211.02419/) (2022)
- [A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging](https://mlsanity.com/paper/2301.01448/) (2023)
- [An image segmentation algorithm based on multi-scale feature pyramid network](https://mlsanity.com/paper/2305.10631/) (2023)
- [From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation](https://mlsanity.com/paper/2606.22168/) (2026)

## Abstract (arXiv)

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. The proposed mU-Net outperformed existing state-of-art networks.