# A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

## TL;DR

This paper introduces a new level-set method for accurately segmenting tumor organoids in images, improving precision and efficiency over existing techniques.

## Contribution

A convexity-preserving level-set model with a novel initialization method for precise tumor organoid segmentation.

## Key findings

- The model achieved an average Dice value of 98.81±0.48% on pancreatic ductal adenocarcinoma organoid images.
- Compared to C-V and CPLSE models, the proposed method is more accurate and faster with an average computation time of 20.67 s.
- The model effectively handles overlapping structures and noise in tumor organoid segmentation.

## Abstract

Tumor organoid cultures play a crucial role in clinical practice, particularly in guiding medication by accurately determining the morphology and size of the organoids. However, segmenting individual tumor organoids is challenging due to their inhomogeneous internal intensity and overlapping structures. This paper proposes a convexity-preserving level-set segmentation 4 model based on the characteristics of tumor organoid images to segment individual tumor organoids precisely. Considering the predominant spherical shape exhibited by organoid growth, we propose a level-set model that includes a data-driven term, a curvature term, and a regularization term. The data-driven term pulls the contour to the vicinity of the boundary; the curvature term ensures the maintenance of convexity in the targeted segmentation, and the regularization term controls the smoothness and propagation of the contour. The proposed model aids in overcoming interference from factors such as overlap and noise, enabling the evolving curve to converge to the actual boundary of the target accurately. Furthermore, we propose a selectable and targeted initialization method that guarantees precise segmentation of specific regions of interest. Experiments on 51 pancreatic ductal adenocarcinoma organoid images show that our model achieved excellent segmentation results. The average Dice value and computation time are 98.81±0.48% and 20.67 s. Compared with the C-V and CPLSE models, it is more accurate and takes less time.