Automated registration of diagnostic to prediagnostic x‐ray mammograms: Evaluation and comparison to radiologists’ accuracy

Pereira, Snehal M Pinto; Hipwell, John H.; McCormack, Valerie; Tanner, Christine; Moss, Sue; Wilkinson, Louise; Khoo, Lisanne; Pagliari, Catriona; Skippage, Pippa; Kliger, Carole J.; Hawkes, David J.; dos‐Santos‐Silva, Isabel

doi:10.1118/1.3457470

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…Prior studies for breast image registration were either on two-dimensional (2-D) mammographic images (41)(42)(43)(44)(45)(46)(47), or on "short-interval" longitudinal breast MR images, which are acquired seconds or minutes apart during one visit [e.g., (48)(49)(50)(51)(52)(53)(54)]. There, registration is used mainly for motion correction (55)(56)(57)(58)(59)(60)(61)(62).…”

Section: Need For Evaluating Registration Performance For Longitudinamentioning

confidence: 99%

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Weinstein

Conant

et al. 2014

Magnetic Resonance in Med

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Purpose To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. Methods Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. Results DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. Conclusions The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

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Section: Need For Evaluating Registration Performance For Longitudinamentioning

confidence: 99%

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Weinstein

Conant

et al. 2014

Magnetic Resonance in Med

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“…To reduce the computational complexity, we uses affine transformation model in the CPD registration method. The details of the implementations can be found in [18,19].…”

Section: S463mentioning

confidence: 99%

A new idea for visualization of lesions distribution in mammogram based on CPD registration method

Pan

et al. 2017

THC

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BACKGROUND: Mammography is currently the most effective technique for breast cancer. Lesions distribution can provide support for clinical diagnosis and epidemiological studies. OBJECTIVE: We presented a new idea to help radiologists study breast lesions distribution conveniently. We also developed an automatic tool based on this idea which could show visualization of lesions distribution in a standard mammogram. METHODS: Firstly, establishing a lesion database to study; then, extracting breast contours and match different women's mammograms to a standard mammogram; finally, showing the lesion distribution in the standard mammogram, and providing the distribution statistics. The crucial process of developing this tool was matching different women's mammograms correctly. We used a hybrid breast contour extraction method combined with coherent point drift method to match different women's mammograms. RESULTS: We tested our automatic tool by four mass datasets of 641 images. The distribution results shown by the tool were consistent with the results counted according to their reports and mammograms by manual. We also discussed the registration error that was less than 3.3 mm in average distance. CONCLUSIONS: The new idea is effective and the automatic tool can provide lesions distribution results which are consistent with radiologists simply and conveniently.

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“…Breast image registration has been utilised for mammographic breast density measurement, e.g. Pereira et al (2010), and as an aid to computer aided detection of mammographic lesions, e.g. van Engeland et al (2003).…”

Section: Breast Image Registration Topicsmentioning

confidence: 99%

“…With a maximum of twelve parameters (for a 3D affine transformation), they are commonly used to initialise a more complex non-rigid registration. However, despite their simplicity, affine transformations have found application in breast image registration, with reported accuracies for mammography registration of around 6 mm (Pereira et al 2010) or 8 mm (van Engeland et al 2003) and for MRI to x-ray registration of 13 mm (Mertzanidou et al 2012b). Arlinghaus et al (2011) applied affine registration to the alignment of DWI-MRI and reported a reduction in the variance of ADC measurements as a consequence.…”

Section: Transformation Modelsmentioning

confidence: 99%

“…This method has also been adapted to register MRI and PET-CT (Dmitriev et al 2013). Application of this transformation model to mammogram registration has generated conflicting results (Diez et al 2010, Pereira et al 2010. Closely related thin-plate splines (Bookstein 1989) have also been used to model the compression deformation of the breast during x-ray mammography (Behrenbruch et al 2003).…”

Section: Transformation Modelsmentioning

confidence: 99%

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A review of biomechanically informed breast image registration

et al. 2016

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Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

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Automated registration of diagnostic to prediagnostic x‐ray mammograms: Evaluation and comparison to radiologists’ accuracy

Abstract: Affine registration accuracy was comparable to that between independent film readers. More advanced two-dimensional nonrigid registration algorithms were incapable of increasing the accuracy of image alignment when compared to affine registration.

Cited by 10 publications

References 27 publications

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

A new idea for visualization of lesions distribution in mammogram based on CPD registration method

A review of biomechanically informed breast image registration

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