Measuring atrophy by simultaneous segmentation of serial MR images using 4-D graph-cuts

Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander; Lötjönen, Jyrki; Rueckert, Daniel

doi:10.1109/isbi.2010.5490147

Cited by 2 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Longitudinal image analysis is an area of growing importance and the detection of subtle longitudinal changes can call for highly accurate segmentation (Reuter et al, 2012). Encouraged by some recent applications (Wolz et al 2010b and Li et al 2014), we believe MAS will be a critical tool for longitudinal biomedical image analysis.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Iglesias, Tang and colleagues later proposed to integrate registration into this generative model (Iglesias et al, 2013c; Tang et al, 2013), which offers a small but significant improvement in segmentation accuracy at an increased computational cost. Finally, many methods that use label fusion to construct a prior in a probabilistic segmentation algorithm (Lotjonen et al, 2009; van der Lijn et al, 2008; Van Der Lijn et al, 2012; Wang et al, 2014c; Wachinger and Golland, 2014; Wolz et al, 2009, 2010b; Platero and Tobar, 2014) can be viewed to be (approximate and/or modified) instantiations of the probabilistic generative label fusion framework.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

“…Along a similar direction, one can apply a refinement to the MAS results, for example, by comparing the observed intensities in the novel image to tissue-based expected intensity profiles (Ledig et al, 2014). Alternatively, label fusion results have been used to compute priors in probabilistic segmentation algorithms (Fortunati et al, 2013; Shi et al, 2010; van der Lijn et al, 2008; Van Der Lijn et al, 2012; Wang et al, 2014c; Wolz et al, 2009, 2010b; Xu et al, 2014b; Platero et al, 2014; Makropoulos et al, 2014; Yan et al, 2014). These methods can be robust to changes in image appearance, for example, in applications where there is significant variation in imaging parameters or the novel subject’s anatomy is not represented in the atlases.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

“…Many methods have been developed to parcellate the whole brain, segmenting it into a large number of regions (Aljabar et al, 2008; Babalola et al, 2009; Fonov et al, 2012; Han et al, 2009; Heckemann et al, 2010, 2011; Keihaninejad et al, 2010; Kotrotsou et al, 2014; Svarer et al, 2005; Wang et al, 2012; Ledig et al, 2014), while other studies have focused on small sets or individual ROIs, such as the caudate nucleus (van Rikxoort et al, 2007b); the cerebellum (Park et al, 2014; Van Der Lijn et al, 2012; Weier et al, 2014); the amygdala (Hanson et al, 2012; Klein-Koerkamp et al, 2014); the corpus callosum (Ardekani et al, 2014; Gao et al, 2014; Meyer, 2014); the striatum (Janes et al, 2014); the subthalamic nucleus, red nucleus and substantia nigra (Xiao et al, 2014b,a); the ventricles (Chou et al, 2008; Raamana et al, 2014); and, most notably, the hippocampus, which has attracted much attention due to its association with dementia and Alzheimer’s disease (Akhondi-Asl et al, 2010; Bishop et al, 2010; Clerx et al, 2013; Hammers et al, 2007; Iglesias et al, 2010; Kim et al, 2012; Leung et al, 2010; Pipitone et al, 2014; Pluta et al, 2012; Raamana et al, 2014; van der Lijn et al, 2008; Van Der Lijn et al, 2012; Winston et al, 2013; Wolz et al, 2010b; Yushkevich et al, 2010; Platero et al, 2014; Ta et al, 2014). …”

Section: Survey Of Applicationsmentioning

confidence: 99%

See 3 more Smart Citations

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

591

457

View full text Add to dashboard Cite

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

show abstract

Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

Section: Survey Of Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

591

457

View full text Add to dashboard Cite

show abstract

“…The related classification accuracies presented recently in the literature have varied between 76% and 94% for controls vs. AD comparison (Vemuri et al, 2008; Fan et al, 2008; Chupin et al, 2009; Wolz et al, 2010; Teipel et al, 2007; Klöppel et al, 2008) and between 65% and 85% for S-MCI vs. P-MCI comparison (Misra et al, 2009; Chupin et al, 2009; Wolz et al, 2010; Teipel et al, 2007). Our results are in concordance with these prior results.…”

Section: Discussionmentioning

confidence: 99%

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

et al. 2011

Self Cite

View full text Add to dashboard Cite

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and comparedtothe conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1%for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.

show abstract

Measuring atrophy by simultaneous segmentation of serial MR images using 4-D graph-cuts

Cited by 2 publications

References 21 publications

Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation of biomedical images: A survey

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

Contact Info

Product

Resources

About