# Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

arXiv: https://arxiv.org/abs/2212.03022 · HTML: https://mlsanity.com/paper/2212.03022/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [What makes for effective detection proposals?](https://mlsanity.com/paper/1502.05082/) (2015)
- [GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation](https://mlsanity.com/paper/1906.11713/) (2019)

## Built on by (most cited first, 7 of 7)

- [CS-TRD: a Cross Sections Tree Ring Detection method](https://mlsanity.com/paper/2305.10809/) (2023)
- [DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector](https://mlsanity.com/paper/2504.16242/) (2025)
- [A Brief Analysis of the Iterative Next Boundary Detection Network for Tree Rings Delineation in Images of Pinus taeda](https://mlsanity.com/paper/2408.14343/) (2024)
- [UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L](https://mlsanity.com/paper/2511.20935/) (2025)
- [Image-based Agarwood Resinous Area Segmentation using Deep Learning](https://mlsanity.com/paper/2404.05129/) (2024)
- [UruDendro, a public dataset of cross-section images of Pinus taeda](https://mlsanity.com/paper/2404.10856/) (2024)
- [TRAS: An Interactive Software for Tracing Tree Ring Cross Sections](https://mlsanity.com/paper/2605.08025/) (2026)

## Abstract (arXiv)

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD.