Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation

Langerak, Thomas; Heide, Uulke A. van der; Kotte, A.N.T.J.; Berendsen, F.F.; Pluim, Josien P. W.

doi:10.1016/j.cviu.2014.09.004

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…When multiple raters provide segmentations from an identical dataset, it is of interest to produce a gold standard. For a voxel representation, a probability map can be constructed, where each voxel value represents the number of raters that counted the voxel as part of their segmentation (Frisoni et al, 2015;Iglesias & Sabuncu, 2015;Langerak, van der Heide, Kotte, Berendsen, & Pluim, 2015; Pipitone et al, 2014). This can be normalized and then thresholded to obtain a binary mask representing whether or not the voxel was segmented by enough rater.…”

Section: Gold Standardmentioning

confidence: 99%

Tractostorm: The what, why, and how of tractography dissection reproducibility

Rheault

Benedictis

Daducci

et al. 2020

Human Brain Mapping

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Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

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Section: Gold Standardmentioning

confidence: 99%

Tractostorm: The what, why, and how of tractography dissection reproducibility

Rheault

Benedictis

Daducci

et al. 2020

Human Brain Mapping

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“…Offline selection aims to select the atlas(es) that outperform other candidate atlases on average in an offline training [38], [40], [45]. Specifically, the process selects atlases that represent the mode of the segmentation performance distribution from the database, and can be estimated empirically offline since the reference contours of all atlases are available.…”

Section: E Offline Atlas Selectionmentioning

confidence: 99%

An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Schipaanboord

Boukerroui

Peressutti

et al. 2019

IEEE Trans. Med. Imaging

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Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This paper presents an analysis of atlas selection methods in the context of radiotherapy treatment planning. For a range of commonly contoured OARs, a thorough comparison of a large class of typical atlas selection methods has been performed. For this evaluation, clinically contoured CT images of the head and neck (N = 316) and thorax (N = 280) were used. The state-of-the-art intensity and deformation similarity-based atlas selection methods were found to compare poorly to perfect atlas selection. Counter-intuitively, atlas selection methods based on a fixed set of representative atlases outperformed atlas selection methods based on the patient image. This study suggests that atlas-based segmentation with currently available selection methods compares poorly to the potential best performance, hampering the clinical utility of atlas-based segmentation. Effective atlas selection remains an open challenge in atlas-based segmentation for radiotherapy planning.

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“…FMRIB's Linear Image Registration Tool (FLIRT) 6,7 in the FMRIB Software Library (FSL) a 8 is an affine registration method, which provides a standard framework for MR images registration based on the image intensity. FLIRT can handle multi-modality medical image (e.g., functional MRI, structure MRI and PET).…”

Section: Fmrib's Linear Image Registration Toolmentioning

confidence: 99%

“…Also, the term σ=mi x i n‖P(y)-P(x i )‖+ε. PBM uses the calculated pairwise similarity between the target patch and the selected candidate patch within the certain region as the voting weight to calculate the soft label of to-be-segmented by the weight sum,(7) where δ(l(x i,k )=c) is a Dirac function, which equals to 1 when l(x i,k )=c and 0 otherwise. Also, PBM obtain the final label of to-be-segmented voxel by Eq.…”

mentioning

confidence: 99%

Multi-Atlas Based Method in Brain MR Image Segmentation

Sun

Zhang

2019

Chinese Medical Sciences Journal

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Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computeraid brain disease analyses. However, the human brain has the complicated anatomical structure. Meanwhile, the brain MR images often suffer from the low intensity contrast around the boundary of ROIs, large inter-subject variance and large inner-subject variance. To address these issues, many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade. In this paper, multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods, conventional methods for label fusion, datasets that have been used for evaluating the multiatlas methods, as well as the applications of multi-atlas based segmentation in clinical researches. We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.

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Improving label fusion in multi-atlas based segmentation by locally combining atlas selection and performance estimation

Cited by 5 publications

References 26 publications

Tractostorm: The what, why, and how of tractography dissection reproducibility

Tractostorm: The what, why, and how of tractography dissection reproducibility

An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

Multi-Atlas Based Method in Brain MR Image Segmentation

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