Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report

TL;DR

This paper introduces a discriminative learning framework for the Random Walks segmentation algorithm, enabling automatic parameter estimation from partially supervised training data, leading to improved segmentation accuracy in medical imaging.

Contribution

It presents a novel latent SVM-based method for automatic parameter tuning in Random Walks segmentation using partially supervised data, addressing a key limitation of manual parameter setting.

Findings

Significantly outperforms baseline methods on clinical MRI datasets.

Effectively handles partially supervised training data.

Improves segmentation accuracy in medical image analysis.

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 optimal 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.