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3D medical images are important components of modern medicine. Their usefulness for the physician depends on their quality, though. Only high-quality images allow accurate and reproducible diagnosis and appropriate support during treatment. We have analyzed 202 MRI images for brain tumor surgery in a retrospective study. Both an experienced neurosurgeon and an experienced neuroradiologist rated each available image with respect to its role in the clinical workflow, its suitability for this specific role, various image quality characteristics, and imaging artifacts. Our results show that MRI data acquired for brain tumor surgery does not always fulfill the required quality standards and that there is a significant disagreement between the surgeon and the radiologist, with the surgeon being more critical. Noise, resolution, as well as the coverage of anatomical structures were the most important criteria for the surgeon, while the radiologist was mainly disturbed by motion artifacts.

In oncological therapy monitoring, the estimation of tumor growth from consecutive CT scans is an important aspect in deciding whether the given treatment is adequate for the patient. This can be done by measuring and comparing the volume of a lesion in the scans based on a segmentation. However, simply counting the voxels within the segmentation mask can lead to significant differences in the volume, if the lesion has been segmented slightly differently by various readers or in different scans, due to the limited spatial resolution of CT and due to partial volume effects. We present a novel algorithm for measuring the volume of liver metastases and lymph nodes which considers partial volume effects at the surface of a lesion. Our algorithm is based on a spatial subdivision of the segmentation. We have evaluated the algorithm on a phantom and a multi-reader study. Our evaluations have shown that our algorithm allows determining the volume more accurately even for larger slice thicknesses. Moreover, it reduces inter-observer variability of volume measurements significantly. The calculation of the volume takes 2 seconds for 50 3 voxels on a single 2.66GHz Intel Core2 CPU.

Segmentation is an essential task in medical image analysis. For example measuring tumor growth in consecutive CT scans based on the volume of the tumor requires a good segmentation. Since manual segmentation takes too much time in clinical routine automatic segmentation algorithms are typically used. However there are always cases where an automatic segmentation fails to provide an acceptable segmentation for example due to low contrast, noise or structures of the same density lying close to the lesion. These erroneous segmentation masks need to be manually corrected. We present a novel method for fast three-dimensional local manual correction of segmentation masks. The user needs to draw only one partial contour which describes the lesion's actual border. This two-dimensional interaction is then transferred into 3D using a live-wire based extrapolation of the contour that is given by the user in one slice. Seed points calculated from this contour are moved to adjacent slices by a block matching algorithm. The seed points are then connected by a live-wire algorithm which ensures a segmentation that passes along the border of the lesion. After this extrapolation a morphological postprocessing is performed to generate a coherent and smooth surface corresponding to the user drawn contour as well as to the initial segmentation. An evaluation on 108 lesions by six radiologists has shown that our method is both intuitive and fast. Using our method the radiologists were able to correct 96.3% of lesion segmentations rated as insufficient to acceptable ones in a median time of 44s.