Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery.

Chuanlu Lin,Tianfu Wang,Yi Wang,Dong Ni

doi:10.3389/fnins.2019.00333

Chuanlu Lin, Tianfu Wang + Show 2 more

Open Access

https://doi.org/10.3389/fnins.2019.00333

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Abstract

The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation.

Highlights

Automated image computing routines that can analyze the magnetic resonance (MR) brain tumor scans are of essential importance for improved disease diagnosis, treatment planning and follow-up of individual patients (Iglesias and Sabuncu, 2015; Mai et al, 2015; Menze et al, 2015; Chen et al, 2018)
We extensively compared our method with state of the art, including Robust Principal Component Analysis (RPCA) (Candès et al, 2011), Robust Alignment by Sparse and Low-rank decomposition (RASL) (Peng et al, 2012), and Spatially COnstraint LOwRank (SCOLOR) (Tang et al, 2018)
Spatial mismatch between different images and adds image alignment into the low-rank based decomposition procedure; the SCOLOR method imposes spatial constraint on sparse component to restrict its structured sparsity

Summary

Introduction

Automated image computing routines (e.g., segmentation, registration, atlas construction) that can analyze the magnetic resonance (MR) brain tumor scans are of essential importance for improved disease diagnosis, treatment planning and follow-up of individual patients (Iglesias and Sabuncu, 2015; Mai et al, 2015; Menze et al, 2015; Chen et al, 2018). Deep learning based approaches require enormous amount of labeled images to train a segmentation model. The recovered images could further be used for atlas construction and specific patient’s follow-up (Joshi et al, 2004; Liu et al, 2014; Zheng et al, 2017; Han et al, 2018). There is lack of deep learning based methods developed for pathological medical image recovery. The low-rank and sparse decomposition (LSD) (Wright et al, 2009; Candès et al, 2011) scheme, learning normal image appearance from unlabeled population data, has been widely employed to decompose pathological MR brain images into recovered normal brain appearances and pathological regions (Liu et al, 2015; Tang et al, 2018)

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