John H. Hipwell

University College London

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David J. Hawkes

University College London

David J. Hawkes

University College London

Thomy Mertzanidou

University College London

Björn Eiben

University College London

Christine Tanner

University of Basel

Vasileios Vavourakis

University of Cyprus

Isabel dos‐Santos‐Silva

London School of Hygiene & Tropical Medicine

Sébastien Ourselin

King's College London

Norman Williams

University College London

Valerie McCormack

Centre International de Recherche sur le Cancer

Cooperative Institutions

University College London

Bellingham Technical College

King's College London

St Thomas' Hospital

University Medical Center

Heidelberg University

Centre National de la Recherche Scientifique

University College Hospital

Wellcome / EPSRC Centre for Interventional and Surgical Sciences

University of Oxford

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Application of XMR 2D-3D Registration to Cardiac Interventional Guidance

Lecture notes in computer science (2003)

Kawal Rhode David Hill Phil Edwards John H. Hipwell Daniel Rueckert

Image registration

Tracking (education)

10.1007/978-3-540-39899-8_37

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Citations (3)

HAMAM – Novel Software Tools for Efficient Assessment of Multi-Modal Breast Imaging

Thorsten Twellmann Nico Karssemeijer Fabian Zöhrer John H. Hipwell Christine Tanner

Poster: ECR 2012 / C-2611 / HAMAM – Novel Software Tools for Efficient Assessment of Multi-Modal Breast Imaging by: Twellmann1, N. Karssemeijer2, F. Zohrer1, J. Hipwell3, C. Tanner4, J. Plath1, T. Bohler1, H. Huisman2, T. Mertzanidou3, R. Blanc4, M. T. Harz1, J. M. Lesniak4, J. Berg5, U. Bick6, K. Schilling7, D. J. Hawkes3, G. Szekely4, M. Hierath8, H. Hahn1; 1Bremen/DE, 2Nijmegen/NL, 3London/UK, 4Zuerich/CH, 5Dundee/UK, 6Berlin/DE, 7Boca Raton, FL/US, 8Vienna/AT

Breast imaging

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Integrating biological knowledge, novel imaging modalities, and modeling in breast cancer diagnosis

Nico Karssemeijer Henkjan Huisman David J. Hawkes John H. Hipwell Tobias Böhler

Despite tremendous advances in modern imaging technology, both early detection and accurate diagnosis of breast cancer are still unresolved challenges. Today, a variety of imaging modalities and image-guided biopsy procedures exist to identify and characterize morphology and function of suspicious breast tissue. However, a clinically feasible solution for breast imaging, which is both highly sensitive and specific with respect to breast cancer, is still missing. As a consequence, unnecessary biopsies are taken and tumours frequently go undetected until a stage where therapy is costly or unsuccessful. Currently, the exact diagnosis of suspicious breast tissue is ambiguous in many cases. To resolve this, computer aided diagnosis methods are developed which use knowledge extracted from large multimodal case databases. Clinical workstations must be developed to allow clinicians to use additional image modalities in an optimal way. Dedicated tools are required to guide clinicians in establishing a diagnosis, and should ultimately lead to more specific and accurate diagnostic decisions.

Modalities

Breast imaging

Modality (human–computer interaction)

10.1109/isbi.2009.5193065

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Citations (1)

A Nonlinear Biomechanical Model Based Registration Method for Aligning Prone and Supine MR Breast Images

IEEE Transactions on Medical Imaging (2014)

Lianghao Han John H. Hipwell Björn Eiben Dean C. Barratt Marc Modat

Preoperative diagnostic magnetic resonance (MR) breast images can provide good contrast between different tissues and 3-D information about suspicious tissues. Aligning preoperative diagnostic MR images with a patient in the theatre during breast conserving surgery could assist surgeons in achieving the complete excision of cancer with sufficient margins. Typically, preoperative diagnostic MR breast images of a patient are obtained in the prone position, while surgery is performed in the supine position. The significant shape change of breasts between these two positions due to gravity loading, external forces and related constraints makes the alignment task extremely difficult. Our previous studies have shown that either nonrigid intensity-based image registration or biomechanical modelling alone are limited in their ability to capture such a large deformation. To tackle this problem, we proposed in this paper a nonlinear biomechanical model-based image registration method with a simultaneous optimization procedure for both the material parameters of breast tissues and the direction of the gravitational force. First, finite element (FE) based biomechanical modelling is used to estimate a physically plausible deformation of the pectoral muscle and the major deformation of breast tissues due to gravity loading. Then, nonrigid intensity-based image registration is employed to recover the remaining deformation that FE analyses do not capture due to the simplifications and approximations of biomechanical models and the uncertainties of external forces and constraints. We assess the registration performance of the proposed method using the target registration error of skin fiducial markers and the Dice similarity coefficient (DSC) of fibroglandular tissues. The registration results on prone and supine MR image pairs are compared with those from two alternative nonrigid registration methods for five breasts. Overall, the proposed algorithm achieved the best registration performance on fiducial markers (target registration error, 8.44 ±5.5 mm for 45 fiducial markers) and higher overlap rates on segmentation propagation of fibroglandular tissues (DSC value > 82%).

Fiducial marker

Image registration

Supine position

Image-guided surgery

Similarity (geometry)

Position (finance)

10.1109/tmi.2013.2294539

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Citations (70)

X-Ray Mammogram Registration: A Novel Validation Method

Lecture notes in computer science (2006)

John H. Hipwell Christine Tanner William R. Crum David J. Hawkes

Image registration

10.1007/11783237_28

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Citations (8)

Detection of stable mammographic features under compression using simulated mammograms

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2010)

Yassir Jafar John H. Hipwell Charlie Tanner D.J. Hawkes

Stable features under simulated mammographic compressions, which will become candidate landmarks for a temporal mammographic feature-based registration algorithm, are discussed in this paper. Using these simulated mammograms, we explore the extraction of features based on standard intensity projection images and local phase projection images. One approach to establishing corresponding features is by template matching using a similarity measure. Simulated mammographic projections from deformed MR volumes are employed, as the mean projected 3D displacements are computed and therefore validation of the technique is possible. Tracking is done by template matching using normalized cross correlation as the similarity measure. The performance of standard projection images and local phase projection images is compared. The preliminary results reveal that although the majority of the points within the breast are difficult to track, a small number may be successfully tracked, which is indicative of their stability and thus their suitability as candidate landmarks. Whilst matching using the standard projection images achieves an overall error of 14.46mm, this error increases to 22.7mm when computing local phase of the projection images. These results suggest that using local phase alone does not improve template matching. For the identification of stable landmarks for feature-based mammogram registration, we conclude that intensity based template matching using normalized correlation is a feasible approach for identifying stable features.

Template matching

Feature (linguistics)

Similarity (geometry)

Similarity measure

Phase congruency

Image registration

10.1117/12.844124

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Citations (0)

Pre-natal exposures and breast tissue composition: findings from a British pre-birth cohort of young women and a systematic review

Breast Cancer Research (2016)

Rachel Denholm Bianca De Stavola John H. Hipwell Simon Doran Marta Cecilia Busana

Breast density, the amount of fibroglandular tissue in the adult breast for a women's age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition.

Surgical oncology

Breast tissue

Breast density

10.1186/s13058-016-0751-z

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Citations (21)

Ultrasound simulation of breast lesions based on a biomechanical model of prone MRI in cancer imaging

Renaud Morin Björn Eiben Luc Bidaut John H. Hipwell Andrew Evans

Ultrasound imaging

Breast imaging

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Citations (0)

Additional file 2: Table S1. of Pre-natal exposures and breast tissue composition: findings from a British pre-birth cohort of young women and a systematic review

Rachel Denholm Bianca De Stavola John H. Hipwell Simon Doran Marta Cecilia Busana

Table (database)

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A New Validation Method for X-ray Mammogram Registration Algorithms Using a Projection Model of Breast X-ray Compression

IEEE Transactions on Medical Imaging (2007)

John H. Hipwell Christine Tanner William R. Crum Julia A. Schnabel David J. Hawkes

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous non- rigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Image registration

Breast MRI

10.1109/tmi.2007.903569

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Citations (36)