# Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method

arXiv: https://arxiv.org/abs/1704.07239 · HTML: https://mlsanity.com/paper/1704.07239/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 19 of 19)

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- [Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model](https://mlsanity.com/paper/2411.12783/) (2024)
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- [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://mlsanity.com/paper/2201.12785/) (2022)
- [SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography](https://mlsanity.com/paper/2103.06419/) (2021)
- [Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting](https://mlsanity.com/paper/2108.00911/) (2021)
- [A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans](https://mlsanity.com/paper/2306.16036/) (2023)
- [Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2206.06575/) (2022)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [View it like a radiologist: Shifted windows for deep learning augmentation of CT images](https://mlsanity.com/paper/2311.14990/) (2023)
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- [Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans](https://mlsanity.com/paper/2303.05126/) (2023)
- [Kidney abnormality segmentation in thorax-abdomen CT scans](https://mlsanity.com/paper/2309.03383/) (2023)
- [Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation](https://mlsanity.com/paper/2108.06761/) (2021)
- [Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images](https://mlsanity.com/paper/2112.01535/) (2021)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage](https://mlsanity.com/paper/2206.00614/) (2022)
- [AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI](https://mlsanity.com/paper/2406.07688/) (2024)
- [Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation](https://mlsanity.com/paper/2510.08116/) (2025)

## Abstract (arXiv)

Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation connections of U-Net [1] and short-range residual connections from ResNet [2]. The model was trained using the 130 LiTS training datasets and achieved an average Dice score of 0.67 when evaluated on the 70 test CT scans, which ranked first for the LiTS challenge at the time of the ISBI 2017 conference.