Integrated Segmentation and Non-linear Registration for Organ Segmentation and Motion Field Estimation in 4D CT Data

Schmidt-Richberg, Alexander; Handels, Heinz; Ehrhardt, Jan

doi:10.3414/me9234

Cited by 18 publications

(6 citation statements)

References 26 publications

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“…For expansion of the extracted paths to the vessel boundaries, a variational level setbased segmentation approach following [23] was used in this work. From a mathematical point of view, the surface of an object is expressed implicitly as the zero-level curve of the level set function φ(x), the so-called zero level set.…”

Section: Level Set Segmentation Of the Connected Vesselsmentioning

confidence: 99%

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

Forkert¹,

Schmidt-Richberg²,

Fiehler³

et al. 2012

Methods Inf Med

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SummaryObjectives: Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graphbased approach. After consistency check, rea- sonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

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Section: Level Set Segmentation Of the Connected Vesselsmentioning

confidence: 99%

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

Forkert¹,

Schmidt-Richberg²,

Fiehler³

et al. 2012

Methods Inf Med

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“…Registration and lobe segmentation are combined following the principle idea of [4] and extending it by multi-object lobe segmentation. In the procedure, segmentations φ T i of the lobes in the template image are assumed to be known.…”

Section: Integrated Lobe Segmentation and Registrationmentioning

confidence: 99%

“…Several recent publications propose combining image registration and a segmentation of the object-of-interest to improve registration in critical regions [3,4]. However, these methods use solely intensity-based segmentation methods that are not applicable for lobe segmentation.…”

Section: Introductionmentioning

confidence: 99%

Lung Registration with Improved Fissure Alignment by Integration of Pulmonary Lobe Segmentation

Schmidt-Richberg

Ehrhardt

Werner

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Accurate registration of human lungs in CT images is required for many applications in pulmonary image analysis and used for example for atlas generation. While various registration approaches have been developed in the past, the correct alignment of the interlobular fissures is still challenging for many reasons, especially for inter-patient registration. Fissures are depicted with very low contrast and their proximity in the image shows little detail due to the lack of vessels. Moreover, iterative registration algorithms usually require the objects to be overlapping in both images to find the right transformation, which is often not the case for fissures.In this work, a novel approach is presented for integrated lobe segmentation and intensity-based registration aiming for a better alignment of the interlobular fissures. To this end, level sets with a shape-based fissure attraction term are used to formulate a new condition in the registration framework. The method is tested for pairwise registration of lung CT scans of nine different subjects and the results show a significantly improved matching of the pulmonary lobes after registration.

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“…These approaches can be classified as model-to-image registration (e.g., [1,2,3]) or as joint segmentation and registration approaches (e.g., [4,5,6]). While in model-to-image registration, segmentation is performed by image registration of a model, joint approaches combine segmentation and registration in a single functional.…”

Section: Introductionmentioning

confidence: 99%

Aortic Arch Quantification using Efficient Joint Segmentation and Registration

Biesdorf

Rohr

Tengg-Kobligk

et al. 2011

Bildverarbeitung Für Die Medizin 2011

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Abstract. Accurate aortic arch quantification is important for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for the quantification of the aortic arch morphology with improved computational efficiency which combines 3D model-based segmentation with intensity-based image registration. The performance of the approach has been evaluated based on 3D synthetic images and clinically relevant 3D CTA images including pathologies. We also performed a quantitative comparison with a previous approach.

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Integrated Segmentation and Non-linear Registration for Organ Segmentation and Motion Field Estimation in 4D CT Data

Abstract: Applied in the field of radiation therapy of thoracic tumors, the presented integrated approach turns out to be useful for simultaneous segmentation and registration by improving the results compared to the application of the methods independently.

Cited by 18 publications

References 26 publications

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

Lung Registration with Improved Fissure Alignment by Integration of Pulmonary Lobe Segmentation

Aortic Arch Quantification using Efficient Joint Segmentation and Registration

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