Computer-aided analysis of prostate multiparametric MR images: an unsupervised fusion-based approach.

OBJECTIVE: The aim of this study is to provide an automatic framework for computer-aided analysis of multiparametric magnetic resonance (mp-MR) images of prostate. METHOD: We introduce a novel method for the unsupervised analysis of the images. An evidential C-means classifier was adapted for use with a segmentation scheme to address multisource data and to manage conflicts and redundancy. RESULTS: Experiments were conducted using data from 15 patients. The evaluation protocol consisted in evaluating the method abilities to classify prostate tissues, showing the same behaviour on the mp-MR images, into homogeneous classes. As the actual diagnosis was available, thanks to the correlation with histopathological findings, the assessment focused on the ability to segment cancer foci. The method exhibited global sensitivity and specificity of 70 and 88 %, respectively. CONCLUSION: The preliminary results obtained by these initial experiments showed that the method can be applied in clinical routine practice to help making decision especially for practitioners with limited experience in prostate MRI analysis.

ProstAtlas: a digital morphologic atlas of the prostate.

Computer-aided medical interventions and medical robotics for prostate cancer have known an increasing interest and research activity. However before the routine deployment of these procedures in clinical practice becomes a reality, in vivo and in silico validations must be undertaken. In this study, we developed a digital morphologic atlas of the prostate. We were interested by the gland, the peripheral zone and the central gland. Starting from an image base collected from 30 selected patients, a mean shape and most important deformations for each structure were deduced using principal component analysis. The usefulness of this atlas was highlighted in two applications: image simulation and physical phantom design.

Zonal segmentation of prostate using multispectral magnetic resonance images.

PURPOSE: To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland. METHODS: The proposed method is based on a modified version of the evidential C-means clustering algorithm. The evidential C-means optimization process was modified to introduce spatial neighborhood information. A priori knowledge of the prostate's zonal morphology was modeled as a geometric criterion and used as an additional data source to enhance the differentiation of the two zones. RESULTS: Thirty-one clinical magnetic resonance imaging series were used to validate the method, and interobserver variability was taken into account in assessing its accuracy. The mean Dice Similarity Coefficient was 89% for the central gland and 80% for the peripheral zone, as validated by a consensus from expert radiologist segmentation. CONCLUSIONS: The method was statistically insensitive to variations in patient age, prostate volume and the presence of tumors, which increases its feasibility in a clinical context.

Prostate cancer characterization on MR images using fractal features.

PURPOSE: Computerized detection of prostate cancer on T2-weighted MR images. METHODS: The authors combined fractal and multifractal features to perform textural analysis of the images. The fractal dimension was computed using the Variance method; the multifractal spectrum was estimated by an adaptation of a multifractional Brownian motion model. Voxels were labeled as tumor/nontumor via nonlinear supervised classification. Two classification algorithms were tested: Support vector machine (SVM) and AdaBoost. RESULTS: Experiments were performed on images from 17 patients. Ground truth was available from histological images. Detection and classification results (sensitivity, specificity) were (83%, 91%) and (85%, 93%) for SVM and AdaBoost, respectively. CONCLUSIONS: Classification using the authors' model combining fractal and multifractal features was more accurate than classification using classical texture features (such as Haralick, wavelet, and Gabor filters). Moreover, the method was more robust against signal intensity variations. Although the method was only applied to T2 images, it could be extended to multispectral MR.

A non rigid registration and deformation algorithm for ultrasound & MR images to guide prostate cancer therapies.

Multimodality image registration is a critical issue in image-guided cancer ablation techniques. Focal therapies of prostate cancer are usually monitored using ultrasound imaging, while the dose planning is performed on MRI. In this study, a new multimodality images registration and deformation method, based on the Thin Plate Splines -Rigid Point Matching (TPS-RPM) algorithm, is introduced. The Method combines non-rigid mapping and interpolation to deform the images. Preliminary results obtained on phantom and clinical images showed that the registration is accurate and robust against landmarks initialization.

Automatic 3D segmentation of prostate in MRI combining a priori knowledge, Markov fields and Bayesian framework.

Accurate localization and contouring of prostate are important issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. Our paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. We use a statistical shape model as a priori knowledge, and we model gray levels distribution by fitting histogram modes with a Gaussian mixture. Markov fields are used to introduce contextual information regarding voxels neighbourhood. Final labelling optimization is based on Bayesian a posteriori classification, estimated with the Iterative Conditional Mode algorithm (ICM). We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 6 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff distance (HD) and Overlap Ratio (OR) were 9.94 mm and 0.83, respectively. Beyond fast computing times, this new method showed satisfying results, even at prostate's base and apex.

A new method based on both fuzzy set and possibility theories for tumor volume segmentation on PET images.

A new automatic method for tumor volume segmentation on PET images has been developed. The method introduced in this paper is based on previous works in MRA segmentation and involves both fuzzy set and possibility theories. Visual results prove the method efficiency which is confirmed by obtained Jaccard index.

[Multiple keratoacanthoma centrifugum marginatum]. / Kératoacanthomes centrifuges marginés multiples.

INTRODUCTION: Keratoacanthoma centrifugum marginatum is a rare variety of commonly isolated keratoacanthoma. The size of the lesions and prolonged evolution often raise therapeutic problems. CASE REPORT: A 63-year-old man presented with multiple keratoacanthoma centrifugum marginatum evolving for 6 months and predominating on the lower limbs. Because of the multiplicity and size of the lesions, the patient was treated at acitretine at the dose of 1 mg/kg/day for five months, leading to the regression of all the lesions. DISCUSSION: Keratoacanthoma centrifugum marginatum has rarely been reported in the literature. Our observation remains exceptional in the multiplicity and size of the lesions. The efficacy of retinoids, previously reported in the treatment of solitary giant keratoacanthoma, multiple keratoacanthoma and keratoacanthoma centrifugum marginatum, was demonstrated even in this handicapping form of keratoacanthoma.