Registration of Breast MRI and 3D Scan Data Based on Surface Matching

Bessa, Silvia; Carvalho, Pedro; Oliveira, Hélder P.

doi:10.1109/isbi.2019.8759306

Cited by 4 publications

(12 citation statements)

References 5 publications

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“…In this paper, breast MRI/3D scan fusion is accomplished with an improved version of the Bessa et al. [ 5 ] algorithm ( Fig. 3 ) that includes the tumor: coarse alignment transformations are applied to the tumor points, which are further deformed on the FFD step - tumor is initially converted to the original grid of control points, and its shape and position is updated with the deformed grid resultant from the surface matching FFD.…”

Section: Methodsmentioning

confidence: 99%

“…[ 15 ] 1 Registration MRI and 3D scan in upright International Conference Bessa et al. [ 5 ] 7 Registration MRI and 3D scan in upright International Conference Duraes et al. [ 19 ] 9 Registration MRI and 3D scan in supine Journal Lee et al.…”

Section: Table A2mentioning

confidence: 99%

“…To extrapolate meaningful clinical data from breast 3D surface scan and MRI combined registration, the validity and reliability of breast image fusion algorithms need to be determined. The purpose of this paper is to validate the MRI/3D fusion algorithm from Bessa et al [5] to create breast cancer 3D models and to approach clinical applications and technological constraints. The influence of the patient's position on the morphology of the breast is quantitatively analyzed with an image acquisition protocol that acquires multi-modal images with breast surface markers and torso fiducial points visible on both the 3D surface scan and MRI data.…”

Section: Introductionmentioning

confidence: 99%

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3D digital breast cancer models with multimodal fusion algorithms

et al. 2020

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Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient’s breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice.

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Section: Methodsmentioning

confidence: 99%

Section: Table A2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D digital breast cancer models with multimodal fusion algorithms

et al. 2020

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“…In [4,5], a multi-modal image registration combines MRI/3D surface scans into a patient-specific breast model that enables precise non-invasive pre-operative location of the tumor and provides a proper 3D upright representation for computer assisted surgery planning [10]. This two-step strategy comprises ICP for initial alignment of interior and surface data, and free form deformation (FFD) for the fine matching of the breast anterior surfaces in both representations.…”

Section: Multimodal Registration and Validationmentioning

confidence: 99%

“…In [4], we proposed a multimodal fusion algorithm that combines prone MRI with 3D surface scans in upright, that was thoroughly validated [5] in its ability to create adequate personalized 3D models of the patient's breast with precise tumor location. However, this strategy relied on manual annotations of the breast anterior contour on the T1-weighted MRI (MRI-T1w) sequence, where the 3D anatomical landmarks and structures are most visible.…”

Section: Introductionmentioning

confidence: 99%

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

Bessa¹,

Teixeira²,

Carvalho³

et al. 2020

Proceedings of 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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Breast conservative surgery (BCS) combined with radiotherapy has become the treatment of choice for the majority of women suffering from breast cancer. Every year approximately 2.1million new cases arise worldwide, but fortunately the 10-year survival rate now exceeds 80% mostly due to the early detection of non-palpable breast cancer. The accurate localization of the tumor is of utmost importance to decrease the re-excision rate and necessity of a second surgery, and also pivotal to the minimization of unaesthetic outcomes caused by these interventions. Nevertheless, 7 out of 10 patients will need invasive pre-operative localization and women will live long lives with the dire consequences of cancer treatments. Currently, it is up to the physicians to correlate multimodal 2-dimensial (2D) sectional images to the 3D space for surgical planning. The challenge is now focused on the technology to create an alternative non-invasive tumor location procedure that can be ethical and fit to the clinical set. This paper describes an automatic image segmentation and registration algorithm that fuses 3D optical scans of the breast surface, with interior radiological data for tumor characterization, to build a personalized digital breast model that can potentially be used as a non-invasive digital pre-operative localization technique, improving tumor visualization and surgery planning.

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A rigged model of the breast for preoperative surgical planning

Mazier

Ribes

Gilles

et al. 2021

Journal of Biomechanics

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In breast surgical practice, drawing is part of the preoperative planning procedure and is essential for a successful operation. In this study, we design a pipeline to assist surgeons with patient-specific breast surgical drawings. We use a deformable torso model containing the surgical patterns to match any breast surface scan. To be compatible with surgical timing, we build an articulated model through a skinning process coupled with shape deformers to enhance a fast registration process. On one hand, the scalable bones of the skinning account for pose and morphological variations of the patients. On the other hand, pre-designed artistic blendshapes create a linear space for guaranteeing anatomical variations. Then, we apply meaningful constraints to the model to find a trade-off between precision and speed. The experiments were conducted on 7 patients, in 2 different poses (prone and supine) with a breast size ranging from 36A and 42C (US/UK bra sizing). The acquisitions were obtained using the depth camera Structure Sensor, and the breast scans were acquired in less than 1 minute. The result is a registration method converging within a few seconds (3 maximum), reaching a Mean Absolute Error of 2.3 mm for mesh registration and 8.0 mm for breast anatomical landmarks. Compared to the existing literature, our model can be personalized and does not require any database. Finally, our registered model can be used to transfer surgical reference patterns onto any patient in any position.

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Registration of Breast MRI and 3D Scan Data Based on Surface Matching

Cited by 4 publications

References 5 publications

3D digital breast cancer models with multimodal fusion algorithms

3D digital breast cancer models with multimodal fusion algorithms

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

A rigged model of the breast for preoperative surgical planning

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