In computer-aided diagnosis of breast MRI, a precise segmentation of the breast is often required as a fundamental step to facilitate further diagnostic tasks, e.g., breast density measurement, lesion detection and automatic reporting. In this work, a fully automatic method dedicated to breast segmentation is proposed, which comprises four major steps: sheet-like structures enhancement, pectoralis muscle boundary segmentation, breast-air boundary segmentation and breast extraction. To validate the proposed method, the segmented breast boundaries of 84 breast MR images, acquired in five different sites with variant imaging protocols, were compared to the manual segmentation. An average distance of 2.56mm with a standard deviation of 3.26mm was achieved

3D printing (rapid prototyping) appears to be feasible. Scenarios for the clinical use of the technology might include presenting the model to the surgeon to provide a better understanding of the tumor's spatial characteristics in the breast, in order to improve decision-making in relation to neoadjuvant chemotherapy or surgical approaches. J. Surg. Oncol. 2017;115:238-242. © 2016 Wiley Periodicals, Inc.

Breast cancer diagnosis based on magnetic resonance images (breast MRI) is increasingly being accepted as an additional diagnostic tool to mammography and ultrasound, with distinct clinical indications.1 Its capability to detect and differentiate lesion types with high sensitivity and specificity is countered by the fact that visual human assessment of breast MRI requires long experience. Moreover, the lack of evaluation standards causes diagnostic results to vary even among experts. The most important MR acquisition technique is dynamic contrast enhanced (DCE) MR imaging since different lesion types accumulate contrast material (CM) differently. The wash-in and wash-out characteristic as well as the morphologic characteristic recorded and assessed from MR images therefore allows to differentiate benign from malignant lesions. In this work, we propose to calculate second order statistical features (Haralick textures) for given lesions based on subtraction and 4D images and on parametermaps. The lesions are classified with a linear classification scheme into probably malignant or probably benign. The method and model was developed on 104 histologically graded lesions (69 malignant and 35 benign). The area under the ROC curve obtained is 0.91 and is already comparable to the performance of a trained radiologist

No abstract

The delineation and diagnosis of non-mass-like lesions, most notably DCIS (ductal carcinoma in situ), is among the most challenging tasks in breast MRI reading. Even for human observers, DCIS is not always easy to diferentiate from patterns of active parenchymal enhancement or from benign alterations of breast tissue. In this light, it is no surprise that CADe/CADx approaches often completely fail to classify DCIS. Of the several approaches that have tried to devise such computer aid, none achieve performances similar to mass detection and classification in terms of sensitivity and specificity. In our contribution, we show a novel approach to combine a newly proposed metric of anatomical breast symmetry calculated on subtraction images of dynamic contrast-enhanced (DCE) breast MRI, descriptive kinetic parameters, and lesion candidate morphology to achieve performances comparable to computer-aided methods used for masses. We have based the development of the method on DCE MRI data of 18 DCIS cases with hand-annotated lesions, complemented by DCE-MRI data of nine normal cases. We propose a novel metric to quantify the symmetry of contralateral breasts and derive a strong indicator for potentially malignant changes from this metric. Also, we propose a novel metric for the orientation of a finding towards a fix point (the nipple). Our combined scheme then achieves a sensitivity of 89% with a specificity of 78%, matching CAD results for breast MRI on masses. The processing pipeline is intended to run on a CAD server, hence we designed all processing to be automated and free of per-case parameters. We expect that the detection results of our proposed non-mass aimed algorithm will complement other CAD algorithms, or ideally be joined with them in a voting scheme