# Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/2009.11717 · HTML: https://mlsanity.com/paper/2009.11717/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM](https://mlsanity.com/paper/2108.08095/) (2021)
- [Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa](https://mlsanity.com/paper/2301.10351/) (2023)
- [rNCA: Self-Repairing Segmentation Masks](https://mlsanity.com/paper/2512.13397/) (2025)
- [UI-VISA: U-Net Initialized Vascular Image Segmentation Architecture](https://mlsanity.com/paper/2609.01598/) (2026)

## Abstract (arXiv)

In this paper we present a methodology that uses convolutional neural networks (CNNs) for segmentation by iteratively growing predicted mask regions in each coordinate direction. The CNN is used to predict class probability scores in a small neighborhood of the center pixel in a tile of an image. We use a threshold on the CNN probability scores to determine whether pixels are added to the region and the iteration continues until no new pixels are added to the region. Our method is able to achieve high segmentation accuracy and preserve biologically realistic morphological features while leveraging small amounts of training data and maintaining computational efficiency. Using retinal blood vessel images from the DRIVE database we found that our method is more accurate than a fully convolutional semantic segmentation CNN for several evaluation metrics.