# Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

arXiv: https://arxiv.org/abs/2301.07475 · HTML: https://mlsanity.com/paper/2301.07475/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 64 references that are in the map.
- First posted on arXiv 18 Jan 2023.

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

- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [Uncertainty guided semi-supervised segmentation of retinal layers in OCT images](https://mlsanity.com/paper/2103.02083/) (2021)
- [Local Intensity Order Transformation for Robust Curvilinear Object Segmentation](https://mlsanity.com/paper/2202.12587/) (2022)
- [Learning-Based Algorithms for Vessel Tracking: A Review](https://mlsanity.com/paper/2012.08929/) (2020)
- [SemiCurv: Semi-Supervised Curvilinear Structure Segmentation](https://mlsanity.com/paper/2205.08706/) (2022)
- [Transformer-Unet: Raw Image Processing with Unet](https://mlsanity.com/paper/2109.08417/) (2021)
- [Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features](https://mlsanity.com/paper/2201.09163/) (2022)
- [Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation](https://mlsanity.com/paper/2203.03631/) (2022)

## Built on by (most cited first, 3 of 3)

- [Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures](https://mlsanity.com/paper/2404.03010/) (2024)
- [MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation](https://mlsanity.com/paper/2305.03617/) (2023)
- [Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention](https://mlsanity.com/paper/2605.10581/) (2026)

## Abstract (arXiv)

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.