Abstract. In this paper, we propose a fully automatic method for the coupled 3D localization and segmentation of lower abdomen structures. We apply it to the joint segmentation of the prostate and bladder in a database of CT scans of the lower abdomen of male patients. A flexible approach on the bladder allows the process to easily adapt to high shape variation and to intensity inhomogeneities that would be hard to characterize (due, for example, to the level of contrast agent that is present). On the other hand, a statistical shape prior is enforced on the prostate. We also propose an adaptive non-overlapping constraint that arbitrates the evolution of both structures based on the availability of strong image data at their common boundary. The method has been tested on a database of 16 volumetric images, and the validation process includes an assessment of inter-expert variability in prostate delineation, with promising results.

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We are interested in the fully automatic delineation of the bladder in CT images in the frame of dose calculation for conformational radiotherapy. To this end we fit a series of 3D deformable templates to the contours of anatomical structures. The novelty of our approach resides in the ability to automatically adapt to different kinds of bladder images (homogenous, non-homogenous, contrasted or non-contrasted). The adaptation of the algorithm to inhomogeneities within the bladder improves the accuracy of the segmentation. We validate our approach on a database of tomodensitometric (CT) images of the lower abdomen of male patients.

Abstract. In this paper we propose a novel learning-based CBIR method for fast content-based retrieval of similar 3D images based on the intrinsic Random Forest (RF) similarity. Furthermore, we allow the combination of flexible user-defined semantics (in the form of retrieval contexts and high-level concepts) and appearance-based (low-level) features in order to yield search results that are both meaningful to the user and relevant in the given clinical case. Due to the complexity and clinical relevance of the domain, we have chosen to apply the framework to the retrieval of similar 3D CT hepatic pathologies, where search results based solely on similarity of low-level features would be rarely clinically meaningful. The impact of high-level concepts on the quality and relevance of the retrieval results has been measured and is discussed for three different set-ups. A comparison study with the commonly used canonical Euclidean distance is presented and discussed as well.

Background: BRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status. Methods: A total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses. Results: Lasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of~4%, suggesting that imaging compensates the predictive information arising from family cancer structure. Conclusions: The L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing.