  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.
