Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation

TL;DR

This paper introduces PAScAL, a novel MRI segmentation method for the anterior visual pathway that combines partitioned shape models with on-the-fly sparse appearance learning, effectively handling healthy and pathological cases.

Contribution

It presents a new partitioned statistical shape model with a refinement process and hierarchical framework, improving segmentation accuracy for abnormal and subtle anatomical variations.

Findings

Significantly outperforms existing segmentation methods on pathological MRI data.

Effective in segmenting both healthy and glioma-affected optic nerves.

Validated on 21 pediatric MRI scans with promising results.

Abstract

MRI quantification of cranial nerves such as anterior visual pathway (AVP) in MRI is challenging due to their thin small size, structural variation along its path, and adjacent anatomic structures. Segmentation of pathologically abnormal optic nerve (e.g. optic nerve glioma) poses additional challenges due to changes in its shape at unpredictable locations. In this work, we propose a partitioned joint statistical shape model approach with sparse appearance learning for the segmentation of healthy and pathological AVP. Our main contributions are: (1) optimally partitioned statistical shape models for the AVP based on regional shape variations for greater local flexibility of statistical shape model; (2) refinement model to accommodate pathological regions as well as areas of subtle variation by training the model on-the-fly using the initial segmentation obtained in (1); (3) hierarchical deformable framework to incorporate scale information in partitioned shape and appearance models. Our method, entitled PAScAL (PArtitioned Shape and Appearance Learning), was evaluated on 21 MRI scans (15 healthy + 6 glioma cases) from pediatric patients (ages 2-17). The experimental results show that the proposed localized shape and sparse appearance-based learning approach significantly outperforms segmentation approaches in the analysis of pathological data.