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Toward automatic atlas-based surgical planning for septoplasty

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2022年第2期17卷 403-411页

作者： Vicory, Jared Garcia, Guilherme J. M. Rhee, John S. Enquobahrie, Andinet Kitware Inc Med Comp Grp Carrboro NC 27510 USA Med Coll Wisconsin Dept Otolaryngol & Commun Sci Milwaukee WI 53226 USA Marquette Univ Dept Biomed Engn Milwaukee WI 53233 USA Med Coll Wisconsin Milwaukee WI 53226 USA

Purpose Surgery for nasal airway obstruction (NAO) has a high failure rate, with up to 50% of patients reporting persistent symptoms postoperatively. Virtual surgery planning has the potential to improve surgical outcomes, but current manual methods are too labor-intensive to be adopted on a large scale. This manuscript introduces an automatic atlas-based approach for performing virtual septoplasties. Methods A cohort of 47 healthy subjects and 26 NAO patients was investigated. An atlas of healthy nasal geometry was constructed. The automatic virtual septoplasty method consists of a multi-stage registration approach to fit the atlas to a target NAO patient, automatically segment the patient's septum and airway, and deform the patient image to have a non-deviated septum. Results Our automatic virtual septoplasty method straightened the septum successfully in 18 out of 26 NAO patients (69% of cases). In these cases, the ratio of the higher to the lower airspace cross-sectional areas in the left and right nasal cavities improved from 1.47 +/- 0.45 to 1.16 +/- 0.33 in the region surrounding the septal deviation, showing that the nasal airway became more symmetric after virtual septoplasty. Conclusion This automated virtual septoplasty technique has the potential to greatly reduce the effort required to perform computational fluid dynamics (CFD) analysis of nasal airflow for NAO surgical planning. Future studies are needed to investigate if virtual surgery planning using this method is predictive of subjective symptoms in NAO patients after septoplasty.

关键词： Nasal airway obstruction Virtual surgery planning Septoplasty atlas-based segmentation

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Automated Multi-class Fetal Cardiac Vessel segmentation in Aortic Arch Anomalies Using T2-Weighted 3D Fetal MRI 1

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7th International Workshop on Perinatal, Preterm and Paediatric Image Analysis (PIPPI)

作者： Gilliland, Paula Ramirez Uus, Alena Van Poppel, Milou P. M. Grigorescu, Irina Steinweg, Johannes K. Lloyd, David F. A. Pushparajah, Kuberan King, Andrew P. Deprez, Maria Kings Coll London Sch Biomed Engn & Imaging Sci London England

ISBN: (数字)9783031171178

ISBN: (纸本)9783031171178;9783031171161

Congenital heart disease (CHD) encompasses a range of cardiac malformations present from birth, representing the leading congenital diagnosis. 3D volumetric reconstructions of T2w black blood fetal MRI provide optimal vessel visualisation, supporting prenatal CHD diagnosis, a key step for optimal patient management. We present a framework for automated multi-class fetal vessel segmentation in the setting where binary manual labels of the vessels region of interest (ROI) are available for training, as well as a multi-class labelled atlas. We combine deep learning label propagation from multi-class labelled condition-specific atlases with 3D Attention U-Net segmentation to achieve the desired multi-class output. We train a single network to segment 12 fetal cardiac vessels for three distinct aortic arch anomalies (double aortic arch, right aortic arch, and suspected coarctation of the aorta). Our segmentation network is trained by combination of a multiclass loss, which uses the propagated multi-class labels;and a binary loss which uses binary labels generated by expert clinicians. Our proposed method outperforms label propagation in accuracy of vessel segmentation, while succeeding in segmenting the anomaly area of all three CHD diagnoses included, achieving a 100% vessel detection rate.

关键词： Automated segmentation Congenital Heart Disease Fetal Cardiac MRI Label Propagation atlas-based segmentation

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Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics

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MEDICAL PHYSICS 2014年第4期41卷 041909页

作者： Xie, Qiuliang Ruan, Dan Univ Calif Los Angeles Dept Radiat Oncol Los Angeles CA 90095 USA

Purpose: To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. Methods: Accurate and efficient automatic structure segmentation is an important task in medical image processing. atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, a target-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. Results: The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors' method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. Conclusions: The proposed method requires a fixed number of nonrigid reg

关键词： atlas-based segmentation label fusion magnetic resonance imaging (MRI) surrogate metric registration

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Faster dense deformable image registration by utilizing both CPU and GPU

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JOURNAL OF MEDICAL IMAGING 2021年第1期8卷 014002页

作者： Ekstrom, Simon Pilia, Martino Kullberg, Joel Ahlstrom, Hakan Strand, Robin Malmberg, Filip Uppsala Univ Dept Surg Sci Sect Radiol Uppsala Sweden Antaros Med Molndal Sweden Uppsala Univ Ctr Image Anal Dept Informat Technol Div Visual Informat & Interact Uppsala Sweden

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： atlas-based segmentation brain MRI deformable image registration graphics processing unit

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Automatic Full Femur segmentation from Computed Tomography Datasets Using an atlas-based Approach 1

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5th Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (MICCAI-MSKI)

作者： Besler, Bryce A. Michalski, Andrew S. Forkert, Nils D. Boyd, Steven K. Univ Calgary Grad Program Biomed Engn Calgary AB Canada Univ Calgary McCaig Inst Bone & Joint Hlth Calgary AB Canada Univ Calgary Dept Radiol Cumming Sch Med Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada

ISBN: (数字)9783319741130

ISBN: (纸本)9783319741130;9783319741123

Automatic segmentation of femurs in clinical computed tomography remains a challenge. Joints degraded by old age are a particularly challenging dataset to segment. The objective of this study is to evaluate existing methods and propose an alternative method for segmentation of femurs in clinical computed tomography datasets for joints degraded by old age. Bilateral hip computed tomography scans of three cadaveric specimens (six femurs) were available for this study. Deformable registration using an affine selection criterion was used for atlas-based segmentation. For comparison, the six femurs were also segmented with two graph-cut algorithms. An automatic graph-cut segmentation algorithm was only able to separate the femur from the pelvis in two of the six femurs due to a limitation of graph-cuts. The atlas-based method produced consistent automatic segmentations for all degraded joints. In conclusion, atlas-based femur segmentation performs considerably better than an automatic graph-cut algorithm when applied to degraded joints.

关键词： atlas-based segmentation Graph-cut Femur Computed tomography

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Testing the accuracy of 3D automatic landmarking via genome-wide association studies

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G3-GENES GENOMES GENETICS 2022年第2期12卷 jkab443页

作者： Savriama, Yoland Tautz, Diethard Max Planck Inst Evolutionary Biol Dept Evolutionary Genet D-24306 Plon Germany

Various advances in 3D automatic phenotyping and landmark-based geometric morphometric methods have been made. While it is generally accepted that automatic landmarking compromises the capture of the biological variation, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to extract an actual biological signal adequately. Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semiautomatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. We find that most significant SNPs and QTLs are not recovered based on the data derived from the automatic landmarking method. Our results thus confirm the notion that information is lost in the automated landmarking procedure although somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question.

关键词： automatic phenotyping atlas-based segmentation 3D landmarking geometric morphometrics skull lower jaws QTL mapping GWAS Mus musculus domesticus

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Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients

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RADIATION ONCOLOGY 2013年第1期8卷 1-7页

作者： Sjoberg, Carl Lundmark, Martin Granberg, Christoffer Johansson, Silvia Ahnesjo, Anders Montelius, Anders Uppsala Univ Dept Radiol Oncol & Radiat Sci SE-75185 Uppsala Sweden Elekta Instrument AB SE-75147 Uppsala Sweden Univ Uppsala Hosp Dept Med Radiat Phys SE-75185 Uppsala Sweden Umea Univ Dept Radiat Sci SE-90187 Umea Sweden

Background: Semi-automated segmentation using deformable registration of selected atlas cases consisting of expert segmented patient images has been proposed to facilitate the delineation of lymph node regions for three-dimensional conformal and intensity-modulated radiotherapy planning of head and neck and prostate tumours. Our aim is to investigate if fusion of multiple atlases will lead to clinical workload reductions and more accurate segmentation proposals compared to the use of a single atlas segmentation, due to a more complete representation of the anatomical variations. Methods: atlases for lymph node regions were constructed using 11 head and neck patients and 15 prostate patients based on published recommendations for segmentations. A commercial registration software (Velocity AI) was used to create individual segmentations through deformable registration. Ten head and neck patients, and ten prostate patients, all different from the atlas patients, were randomly chosen for the study from retrospective data. Each patient was first delineated three times, (a) manually by a radiation oncologist, (b) automatically using a single atlas segmentation proposal from a chosen atlas and (c) automatically by fusing the atlas proposals from all cases in the database using the probabilistic weighting fusion algorithm. In a subsequent step a radiation oncologist corrected the segmentation proposals achieved from step (b) and (c) without using the result from method (a) as reference. The time spent for editing the segmentations was recorded separately for each method and for each individual structure. Finally, the Dice Similarity Coefficient and the volume of the structures were used to evaluate the similarity between the structures delineated with the different methods. Results: For the single atlas method, the time reduction compared to manual segmentation was 29% and 23% for head and neck and pelvis lymph nodes, respectively, while editing the fused atlas proposal res

关键词： atlas-based segmentation Radiotherapy Head and neck Prostate Delineation time Multi-atlas segmentation

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Multi-atlas segmentation of the skeleton from whole-body MRI-Impact of iterative background masking

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MAGNETIC RESONANCE IN MEDICINE 2020年第5期83卷 1851-1862页

作者： Ceranka, Jakub Verga, Sabrina Kvasnytsia, Maryna Lecouvet, Frederic Michoux, Nicolas de Mey, Johan Raeymaekers, Hubert Metens, Thierry Absil, Julie Vandemeulebroucke, Jef Vrije Univ Brussel Dept Elect & Informat Pleinlaan 2 Brussels Belgium IMEC Leuven Belgium Vrije Univ Brussel Dept Elect Informat & Bioengn Brussels Belgium Catholic Univ Louvain IREC Clin Univ St Luc Brussels Belgium Univ Ziekenhuis Brussel Dept Radiol Brussels Belgium Univ Libre Bruxelles Dept Radiol ULB Hop Erasme Brussels Belgium

Purpose To improve multi-atlas segmentation of the skeleton from whole-body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation. Methods An improved atlas registration scheme is proposed. Starting from a suitable initial alignment, the alignment is refined by introducing additional stages of deformable registration during which the image sampling is limited to the dilated atlas segmentation label mask. The performance of the method was demonstrated using leave-one-out cross-validation using atlases of 10 whole-body 3D-T-1 images of prostate cancer patients with bone metastases and healthy male volunteers, and compared to existing state of the art. Both registration accuracy and resulting segmentation quality, using four commonly used label fusion strategies, were evaluated. Results The proposed method showed significant improvement in registration and segmentation accuracy with respect to the state of the art for all validation criteria and label fusion strategies, resulting in a Dice coefficient of 0.887 (STEPS label fusion). The average Dice coefficient for the multi-atlas segmentation showed over 11% improvement with a decrease of false positive rate from 28.3% to 13.2%. For this application, repeated application of the background masking did not lead to significant improvement of the segmentation result. Conclusions A registration strategy, relying on the use of atlas segmentations as mask during image registration was proposed and evaluated for multi-atlas segmentation of whole-body MRI. The approach significantly improved registration and final segmentation accuracy and may be applicable to other structures of interest.

关键词： atlas-based segmentation bone segmentation image registration whole-body MRI

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Multi-atlas segmentation of the facial nerve from clinical CT for virtual reality simulators

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第2期15卷 259-267页

作者： Gare, Bradley M. Hudson, Thomas Rohani, Seyed A. Allen, Daniel G. Agrawal, Sumit K. Ladak, Hanif M. Western Univ Elect & Comp Engn Grad Program London ON Canada Western Univ Schulich Sch Med London ON Canada Western Univ Dept Otolaryngol Head & Neck Surg London ON Canada Western Univ Dept Med Biophys London ON Canada Western Univ Dept Elect & Comp Engn London ON Canada Univ Western Ontario Dept Elect & Comp Engn London ON N6A 3K7 Canada

Purpose To create a novel, multi-atlas-based segmentation algorithm of the facial nerve (FN) requiring minimal user intervention that could be easily deployed into an existing open-source toolkit. Specifically, the mastoid, tympanic and labyrinthine segments of the FN would be segmented. Methods High-resolution micro-computed tomography (micro-CT) scans were pre-segmented and used as atlases of the FN. The algorithm requires the user to place four fiducials to orient the target, low-resolution clinical CT scan, and generate a centerline along the nerve. based on this data, the appropriate atlas is chosen by the algorithm and then rigidly and non-rigidly registered to provide an automated segmentation of the FN. Results The algorithm was successfully developed and implemented into an existing open-source software framework. Validation was performed on 28 temporal bones, where the automated segmentation was compared against gold-standard manual segmentation by an expert. The algorithm achieved an average Dice metric of 0.76 and an average Hausdorff distance of 0.17 mm for the tympanic and mastoid portions of the FN when segmenting healthy facial nerves, which are similar to previously published algorithms. Conclusion A successful FN segmentation algorithm was developed using a high-resolution micro-CT multi-atlas approach. The algorithm was unique in its ability to segment the entire intratemporal FN, with the exception of the meatal segment, which was not included in the segmentation as it was not discernible from the vestibulocochlear nerve within the internal auditory canal. It will be published as an open-source extension to allow use in virtual reality simulators for automatic segmentation, greatly reducing the time for expert segmentation and verification.

关键词： Facial nerve Image segmentation Image registration Otolaryngology atlas-based segmentation

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Comparison of atlas-based techniques for whole-body bone segmentation

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MEDICAL IMAGE ANALYSIS 2017年 36卷 98-112页

作者： Arabi, Hossein Zaidi, Habib Univ Hosp Geneva Div Nucl Med & Mol Imaging CH-1211 Geneva Switzerland Univ Geneva Geneva Neurosci Ctr CH-1205 Geneva Switzerland Univ Groningen Univ Med Ctr Groningen Dept Nucl Med & Mol Imaging NL-9700 RB Groningen Netherlands Univ Southern Denmark Dept Nucl Med DK-500 Odense Denmark

We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CTIMR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 +/- 10.05) compared to non-weighted IA (DSC = 0.60 +/- 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 +/- 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 +/- 0.05 and 0.75 +/- 0:04, respectively. Despite very long computation time, the extracted bone obtained fro

关键词： MRI PET/MRI Bone segmentation atlas-based segmentation Whole-body

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