Discriminative Parameter Estimation for Random Walks Segmentation

TL;DR

This paper introduces a discriminative learning framework for the Random Walks segmentation algorithm that automatically estimates parameters from training data, improving accuracy on clinical MRI datasets.

Contribution

It presents a novel latent SVM-based method for parameter estimation in Random Walks segmentation, handling weakly supervised training data.

Findings

Significant improvement over baseline methods on clinical MRI data

Effective parameter estimation from weakly supervised samples

Enhanced segmentation accuracy in medical imaging

Abstract

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Speci cally, they provide a hard segmentation of the images, instead of a proba- bilistic segmentation. We overcome this challenge by treating the opti- mal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach signi cantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.