# Liver lesion segmentation informed by joint liver segmentation

arXiv: https://arxiv.org/abs/1707.07734 · HTML: https://mlsanity.com/paper/1707.07734/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

## Built on by (most cited first, 18 of 18)

- [Recurrent Mask Refinement for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2108.00622/) (2021)
- [Efficient Medical Image Segmentation Based on Knowledge Distillation](https://mlsanity.com/paper/2108.09987/) (2021)
- [Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR](https://mlsanity.com/paper/2202.06373/) (2022)
- [Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation](https://mlsanity.com/paper/2105.14732/) (2021)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT](https://mlsanity.com/paper/2108.00831/) (2021)
- [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://mlsanity.com/paper/2201.12785/) (2022)
- [CT Liver Segmentation via PVT-based Encoding and Refined Decoding](https://mlsanity.com/paper/2401.09630/) (2024)
- [Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2206.06575/) (2022)
- [Segmentation by registration-enabled SAM prompt engineering using five reference images](https://mlsanity.com/paper/2407.17933/) (2024)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images](https://mlsanity.com/paper/2201.00895/) (2022)
- [Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network](https://mlsanity.com/paper/2203.03640/) (2022)
- [Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI](https://mlsanity.com/paper/2101.06979/) (2021)
- [Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation](https://mlsanity.com/paper/2108.06761/) (2021)
- [Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers](https://mlsanity.com/paper/2201.10981/) (2022)
- [Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation](https://mlsanity.com/paper/2510.08116/) (2025)
- [Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework](https://mlsanity.com/paper/2512.07574/) (2025)

## Abstract (arXiv)

We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes. We build the model from two fully convolutional networks, connected in tandem and trained together end-to-end. We evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge, attaining competitive liver and liver lesion detection and segmentation scores across a wide range of metrics. Unlike other top performing methods, our model output post-processing is trivial, we do not use data external to the challenge, and we propose a simple single-stage model that is trained end-to-end. However, our method nearly matches the top lesion segmentation performance and achieves the second highest precision for lesion detection while maintaining high recall.