# CS-TRD: a Cross Sections Tree Ring Detection method

arXiv: https://arxiv.org/abs/2305.10809 · HTML: https://mlsanity.com/paper/2305.10809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 3 of 3)

- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections](https://mlsanity.com/paper/2212.03022/) (2022)
- [Automatic Tree Ring Detection using Jacobi Sets](https://mlsanity.com/paper/2010.08691/) (2020)

## Built on by (most cited first, 2 of 2)

- [Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation](https://mlsanity.com/paper/2404.01952/) (2024)
- [DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector](https://mlsanity.com/paper/2504.16242/) (2025)

## Abstract (arXiv)

This work describes a Tree Ring Detection method for complete Cross-Sections of Trees (CS-TRD) that detects, processes and connects edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector (sigma), a resize factor, the number of rays, and the pith location. The first five are fixed by default. The pith location can be marked manually or using an automatic pith detection algorithm. Besides the pith localization, CS-TRD is fully automated and achieves an F-Score of 89% in the UruDendro dataset (of Pinus taeda) and 97% in the Kennel dataset (of Abies alba) without specialized hardware requirements.