A method to detect landmark pairs accurately between intra‐patient volumetric medical images

Yang, Deshan; Zhang, Miao; Chang, Xiao; Fu, Yabo; Shi, Lukui; Li, Harold H.; Mutic, Sasa; Duan, Ye

doi:10.1002/mp.12526

Cited by 16 publications

(21 citation statements)

References 63 publications

(121 reference statements)

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“…TRE was calculated using 500 landmark pairs per case that were manually identified across the abdominal region of patients 1–3. A large number of landmark pairs (>6000) were automatically detected using a toolkit which is based on a 3D scale‐invariant feature transform (SIFT) detection method and a 3D Harris‐Laplacian corner detection method . After manual verification, the top 500 landmark pairs of the best matching quality were chosen.…”

Section: Resultsmentioning

confidence: 99%

“…A large number of landmark pairs (>6000) were automatically detected using a toolkit which is based on a 3D scale-invariant feature transform (SIFT) detection method and a 3D Harris-Laplacian corner detection method. 45 After manual verification, the top 500 landmark pairs of the best matching quality were chosen. Some of the landmarks were shown in Fig.…”

Section: A Lower Lung and Abdomenmentioning

confidence: 99%

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An adaptive motion regularization technique to support sliding motion in deformable image registration

Shi

et al. 2018

Medical Physics

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Section: Resultsmentioning

confidence: 99%

Section: A Lower Lung and Abdomenmentioning

confidence: 99%

An adaptive motion regularization technique to support sliding motion in deformable image registration

Shi

et al. 2018

Medical Physics

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“…, panel A) and applied to abdominal 4DCT data treated with carbon ions, quantifying DIR errors below the voxel resolution. Similarly, a large number of landmarks were also identified for a dense representation in head & neck CT, lung 4DCT, and pelvic MRI by means of improved landmark matching . Limitations of these approaches are present in terms of computational cost, whereas advantages rely on using the dense set of landmarks to drive the DIR with subsequently improved results in the registration accuracy …”

Section: The Geometric Accuracy Paradigmmentioning

confidence: 99%

“…Similarly, a large number of landmarks were also identified for a dense representation in head & neck CT, lung 4DCT, and pelvic MRI by means of improved landmark matching. 109 Limitations of these approaches are present in terms of computational cost, whereas advantages rely on using the dense set of landmarks to drive the DIR with subsequently improved results in the registration accuracy. 77,107 Automatic landmark extraction has been successfully applied in intra-modal imaging, as for CT/CBCT, 26,104 whereas peculiar feature descriptors for landmark correspondence identification require to be defined and evaluated for more complex multi-modal imaging.…”

Section: B Automatic Strategies: Image-basedmentioning

confidence: 99%

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

et al. 2018

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Over the last few decades, deformable image registration (DIR) has gained popularity in image-guided radiation therapy for a number of applications, such as contour propagation, dose warping, and accumulation. Although this raises promising perspectives for the improvement of treatment outcomes and quality of radiotherapy clinical practice, the variety of proposed DIR algorithms, combined with the lack of an effective quantitative quality control metric of the registration, is slowing the transfer of DIR into the clinical routine. Recently, a task group (AAPM TG132) report was published outlining the essential aspects of DIR for image guidance in radiotherapy. However, an accurate and efficient patient-specific validation is not yet defined, and appropriate metrics should be identified to achieve the definition of both geometric and dosimetric accuracy. In this respect, the use of a dense set of anatomical landmarks, along with additional evaluations on contours or deformation field analysis, are likely to drive patient-specific DIR validation in clinical image-guided radiotherapy applications to account for geometric inaccuracies. Automatic and efficient strategies able to provide spatial information of DIR uncertainties and to evaluate monomodal and multimodal image registration, as well as to describe homogenous and un-contrasted regions are believed to represent the future direction in DIR validation. But especially in the case of DIR applications for dose mapping and accumulation, the need of accurate patient-specific validation is not only limited to the evaluation of geometric accuracy. In fact, the need to account for dosimetric inaccuracies due to DIR represents another important area in the field of adaptive treatments. Different approaches are currently being investigated to quantify the effect of DIR error on dose analysis, mainly relying on clinically relevant dose metrics, or on the study of deformation field properties for a voxel-by-voxel evaluation. However, novel research is required for the definition of dedicated and personalized measures capable to relate the geometric and dosimetric inaccuracies, thus bearing useful information for a safe use of DIR by clinical end users. In this paper we provide insights on DIR results evaluation on a patient-specific basis, facing the issues of both geometric and dosimetric paradigms. Challenges on DIR validation are overviewed and discussed, in order to push preliminary clinical guidelines forward on this fundamental topic and boost the implementation of more robust and reliable patient-specific evaluation metrics.

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“…Multiple studies have been published to automate the landmark pair selection task. Yang et al recently developed a method to automatically detect large numbers of landmark pairs . Two pools of landmarks were detected separately using scale‐invariant feature transform (SIFT) and Harris‐Laplacian corner detection algorithms on the two image volumes.…”

Section: Introductionmentioning

confidence: 99%

Automatic large quantity landmark pairs detection in 4DCT lung images

Thomas

et al. 2019

Medical Physics

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Purpose To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy. Methods A large number of landmark pairs were detected within the lung between the end‐exhalation (EE) and end‐inhalation (EI) phases of the lung four‐dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris‐Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi‐stream pseudo‐siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three‐dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs. Results Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm. Conclusion A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

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A method to detect landmark pairs accurately between intra‐patient volumetric medical images

Cited by 16 publications

References 63 publications

An adaptive motion regularization technique to support sliding motion in deformable image registration

An adaptive motion regularization technique to support sliding motion in deformable image registration

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

Automatic large quantity landmark pairs detection in 4DCT lung images

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