# CT Liver Segmentation via PVT-based Encoding and Refined Decoding

arXiv: https://arxiv.org/abs/2401.09630 · HTML: https://mlsanity.com/paper/2401.09630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing](https://mlsanity.com/paper/2303.07428/) (2023)
- [TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2206.08985/) (2022)
- [Liver lesion segmentation informed by joint liver segmentation](https://mlsanity.com/paper/1707.07734/) (2017)
- [NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy](https://mlsanity.com/paper/2104.11138/) (2021)

## Built on by (most cited first, 6 of 6)

- [RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation](https://mlsanity.com/paper/2501.08458/) (2025)
- [A Reverse Mamba Attention Network for Pathological Liver Segmentation](https://mlsanity.com/paper/2502.18232/) (2025)
- [When CNNs Outperform Transformers and Mambas: Revisiting Deep Architectures for Dental Caries Segmentation](https://mlsanity.com/paper/2511.14860/) (2025)
- [MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation](https://mlsanity.com/paper/2405.06166/) (2024)
- [ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation](https://mlsanity.com/paper/2412.19713/) (2024)
- [CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation](https://mlsanity.com/paper/2401.16886/) (2024)

## Abstract (arXiv)

Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver disease diagnosis, disease progression, and treatment planning. In response to the need, we propose a novel deep learning approach, textitPVTFormer, that is built upon a pretrained pyramid vision transformer (PVT v2) combined with advanced residual upsampling and decoder block. By integrating a refined feature channel approach with a hierarchical decoding strategy, PVTFormer generates high quality segmentation masks by enhancing semantic features. Rigorous evaluation of the proposed method on Liver Tumor Segmentation Benchmark (LiTS) 2017 demonstrates that our proposed architecture not only achieves a high dice coefficient of 86.78%, mIoU of 78.46%, but also obtains a low HD of 3.50. The results underscore PVTFormer's efficacy in setting a new benchmark for state-of-the-art liver segmentation methods. The source code of the proposed PVTFormer is available at urlhttps://github.com/DebeshJha/PVTFormer.