Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

TL;DR

This paper reviews the current state of automated segmentation methods for human treelike tubular structures in medical images, highlighting datasets, algorithms, challenges, and future research directions.

Contribution

It provides a comprehensive overview of existing datasets, segmentation techniques, and evaluation metrics, and discusses future challenges and perspectives in the field.

Findings

Summarizes publicly available datasets for tubular structure segmentation.

Reviews various segmentation algorithms and their performance.

Discusses current challenges and future research directions.

Abstract

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.