Hybrid Forests for Left Ventricle Segmentation using only the first slice label
This addresses the problem of reducing labeling effort for medical imaging experts, though it is incremental as it builds on existing Random Forest methods.
The paper tackles the high cost of manual labeling in MRI segmentation by proposing a method that requires only the first slice to be labeled, using it to iteratively infer and train on subsequent slices, achieving promising results for left ventricle segmentation.
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.