# Hybrid Cascaded Neural Network for Liver Lesion Segmentation

arXiv: https://arxiv.org/abs/1909.04797 · HTML: https://mlsanity.com/paper/1909.04797/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

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

- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network](https://mlsanity.com/paper/2112.09135/) (2021)
- [ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation](https://mlsanity.com/paper/2412.19713/) (2024)
- [MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation](https://mlsanity.com/paper/2105.04508/) (2021)
- [PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation](https://mlsanity.com/paper/2409.12678/) (2024)

## Abstract (arXiv)

Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper we propose a cascaded system that combines both 2D and 3D convolutional neural networks to effectively segment hepatic lesions. Our 2D network operates on a slice by slice basis to segment the liver and larger tumors, while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per case of 68.1%, which performs the best among all non pre-trained models and the second best among published methods. We also perform two-fold cross-validation to reveal the over- and under-segmentation issues in the LiTS annotations.