Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling

Ellingsen, Lotta M.; Roy, Snehashis; Carass, Aaron; Blitz, Ari M.; Pham, Dzung L.; Prince, Jerry L.

doi:10.1117/12.2216511

Cited by 14 publications

(12 citation statements)

References 24 publications

(27 reference statements)

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“…Furthermore, since they implement a long processing pipeline together with the atlas-based labelling strategy, the segmentation operation is time consuming [4]. Limitations of these approaches, such as the lack of accuracy on various brain structure boundaries, have been documented [24,25,26,3].…”

Section: Atlas-based Methodsmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

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Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.

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Section: Atlas-based Methodsmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

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“…The performance of the VParNet was compared with four state-of-the-art brain segmentation methods: FreeSurfer (version 6.0.0) ( Dale et al, 1999 ; Fischl et al, 2002 ; Fischl, 2012 ), MALPEM ( Ledig et al, 2015 ), Joint label fusion (JLF) from the ANTs software package ( Wang et al, 2013 ; Wang and Yushkevich, 2013 ), and RUDOLPH ( Ellingsen et al, 2016 ; Carass et al, 2017 ; Shao et al, 2018a ). We also conducted an ablation analysis of the VParNet to see how different strategies affect the performance.…”

Section: Methodsmentioning

confidence: 99%

“…One reason for some of these failures is that most methods rely on registration between the atlas and subject images, which in pathological cases is rarely optimal. One recent algorithm, called RUDOLPH ( Ellingsen et al, 2016 ; Carass et al, 2017 ; Shao et al, 2018a ), was specifically designed to segment subjects with enlarged ventricles. Although robust in ventricle parcellation, this method takes hours to process a single image.…”

Section: Introductionmentioning

confidence: 99%

“… Joint label fusion ( Wang et al, 2013 ) ✓ ✓ Multi-atlas label fusion method formulating a weighted voting scheme to minimize the total expectation of the labeling error. RUDOLPH ( Ellingsen et al, 2016 ; Carass et al, 2017 ; Shao et al, 2018a ) ✓ ✓ Combines tissue segmentation and multi-atlas segmentation to correct registration priors. Designed for subjects with ventriculomegaly.…”

Section: Introductionmentioning

confidence: 99%

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Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Shao

Han

Carass

et al. 2019

NeuroImage: Clinical

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Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly ( p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.

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“…To address this problem in our work, we incorporate a patch-based segmentation method [11,12] to provide a prior for a multi-atlas label fusion framework [9]. Our method, known as r ob u st d icti o nary-learning and l abel p ropagation h ybrid (RUDOLPH) [7], provides a parcellation of the entire brain, providing 138 brain labels (in the cerebellum and cerebrum) while performing accurate ventricular segmentation even with enlarged ventricles. We present a detailed evaluation of this method with respect to the four main cavities of the ventricular system of NPH patients (noting that RUDOLPH also provides a parcellation of the whole brain, examples of which can be seen in Fig.…”

Section: Introductionmentioning

confidence: 99%

Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients

Carass

Shao

et al. 2017

Patch-Based Techniques in Medical Imaging

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Numerous brain disorders are associated with ventriculomegaly; normal pressure hydrocephalus (NPH) is one example. NPH presents with dementia-like symptoms and is often misdiagnosed as Alzheimer’s due to its chronic nature and nonspecific presenting symptoms. However, unlike other forms of dementia NPH can be treated surgically with an over 80% success rate on appropriately selected patients. Accurate assessment of the ventricles, in particular its sub-compartments, is required to diagnose the condition. Existing segmentation algorithms fail to accurately identify the ventricles in patients with such extreme pathology. We present an improvement to a whole brain segmentation approach that accurately identifies the ventricles and parcellates them into four sub-compartments. Our work is a combination of patch-based tissue segmentation and multi-atlas registration-based labeling. We include a validation on NPH patients, demonstrating superior performance against state-of-the-art methods.

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Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling

Cited by 14 publications

References 24 publications

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients

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