Hybrid Forests for Left Ventricle Segmentation using only the first slice label

TL;DR

This paper introduces a novel MRI segmentation approach that reduces manual labeling by leveraging sequential MRI slice structure and combining Random Forest algorithms, achieving promising results in left ventricle segmentation.

Contribution

It presents a semi-automated segmentation method using only the first slice label and iterative inference, combining classical and Mondrian Forests for efficient MRI segmentation.

Findings

Effective segmentation with minimal manual labeling

Iterative inference improves accuracy over slices

Potential for automatic label generation

Abstract

Machine learning models produce state-of-the-art results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is very time consuming and costs experts work. Therefore finding a way to reduce this cost is on high demand. In this paper, we propose a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task. Only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation. Inference result is another datum used to train the model again. The updated model then infers the third slice and the same process is carried out until the last slice. The proposed model is an combination of two Random Forest algorithms: the classical one and a recent one namely Mondrian Forests. We applied our method on human left ventricle segmentation and results are very promising. This method can also be used to generate labels.