# Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1906.01795 · HTML: https://mlsanity.com/paper/1906.01795/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

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

- [Dynamic Linear Transformer for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/2206.00771/) (2022)
- [Chronological age estimation of lateral cephalometric radiographs with deep learning](https://mlsanity.com/paper/2101.11805/) (2021)
- [L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation](https://mlsanity.com/paper/2102.05971/) (2021)
- [Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2302.06356/) (2023)
- [Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning](https://mlsanity.com/paper/2405.12367/) (2024)
- [DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images](https://mlsanity.com/paper/2410.02129/) (2024)

## Abstract (arXiv)

Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.