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A 3D ultrasound computer tomography (USCT) device with a nearly isotropic and spatially invariant 3D point spread function has been constructed at Institute for Data Processing and Electronic (IPE), Karlsruhe Institute of Technology (KIT). This device is currently applied in clinical studies for breast cancer screening. In this paper, a new method to develop an automated segmentation algorithm for USCT acquired images is proposed. The method employs distance regularized level set evolutionary (DRLSE) active contours along with surface fitting extrapolation and 3D binary mask generation for fully automatic segmentation outcome. In the first stage of the proposed algorithm, DRLSE is applied to those 3D USCT slice images which contain breast and are less affected by noise and ring artifacts named as Cat2. The DRLSE segmentation results are employed to extrapolate the rest of slice images known as Cat1. To overcome defectively segmented slice images, a 3D binary mask is generated out of USCT attenuation images. The 3D binary mask is multiplied by the DRLSE-based segmentation results to form finally segmented 3D USCT images. The method was tested on 12 clinical dataset images. According to F-measure criterion, the proposed method shows higher performance than the previously proposed semiautomatic segmentation one.

Breast cancer is the most common cancer among women. The established screening method to detect breast cancer is X-ray mammography. Additionally, MRI is used for diagnosis in clinical routine. Due to complementary diagnostic information, both modalities are often read in combination. Yet, the correlation is challenging due to different dimensionality of images and different patient positioning. In this paper, we describe a method to fuse X-ray mammograms with DCE-MRI. The present study was conducted to evaluate the feasibility of the approach.For the combination of information from both modalities, the images have to be registered using a compression simulation based on a patient-specific biomechanical model. The registered images can be compared directly. The contrast enhancement in the DCE-MRI volume is evaluated using parametric enhancement maps. A projection image of the contrast enhancement is created. The image fusion combines it with X-ray mammograms for intuitive multimodal diagnosis.The image fusion was evaluated using 11 clinical datasets. For 10 of 11 datasets, a good accuracy of the image registration was achieved. The overlap of contrast-enhanced regions with marked lesions in the mammogram is 61%. Lesions are clearly differentiable from surrounding tissue by the DCE-MRI projection in 10 of 11 cases.The described preliminary results are promising, thus we expect the visualization of quantitative information from dynamic MRI together with mammograms to be beneficial for multimodal diagnosis. Because of the use of clinical standard modalities, no additional image acquisition is needed.