Jpw Josien Pluim

Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE)

In a multi-atlas based segmentation procedure, propagated atlas segmentations must be combined in a label fusion process. Some current methods deal with this problem by using atlas selection to construct an atlas set either prior to or after registration. Other methods estimate the performance of propagated segmentations and use this performance as a weight in the label fusion process. This paper proposes a selective and iterative method for performance level estimation (SIMPLE), which combines both strategies in an iterative procedure. In subsequent iterations the method refines both the estimated performance and the set of selected atlases. For a dataset of 100 MR images of prostate cancer patients, we show that the results of SIMPLE are significantly better than those of several existing methods, including the STAPLE method and variants of weighted majority voting.

Registration of Cervical MRI Using Multifeature Mutual Information

Radiation therapy for cervical cancer can benefit from image registration in several ways, for example by studying the motion of organs, or by (partially) automating the delineation of the target volume and other structures of interest. In this paper, the registration of cervical data is addressed using mutual information (MI) of not only image intensity, but also features that describe local image structure. Three aspects of the registration are addressed to make this approach feasible. First, instead of relying on a histogram-based estimation of mutual information, which poses problems for a larger number of features, a graph-based implementation of alpha-mutual information (alpha-MI) is employed. Second, the analytical derivative of alpha-MI is derived. This makes it possible to use a stochastic gradient descent method to solve the registration problem, which is substantially faster than non-derivative-based methods. Third, the feature space is reduced by means of a principal component analysis, which also decreases the registration time. The proposed technique is compared to a standard approach, based on the mutual information of image intensity only. Experiments are performed on 93 T2-weighted MR clinical data sets acquired from 19 patients with cervical cancer. Several characteristics of the proposed algorithm are studied on a subset of 19 image pairs (one pair per patient). On the remaining data (36 image pairs, one or two pairs per patient) the median overlap is shown to improve significantly compared to standard MI from 0.85 to 0.86 for the clinical target volume (CTV, p = 2 ldr10-2), from 0.75 to 0.81 for the bladder (p = 8 ldr 10-6), and from 0.76 to 0.77 for the rectum (p = 2 ldr 10-4). The registration error is improved at important tissue interfaces, such as that of the bladder with the CTV, and the interface of the rectum with the uterus and cervix.

Semi-automatic construction of reference standards for evaluation of image registration

Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic fashion. A well-distributed set of n landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers define corresponding anatomic locations in the second scan for a specified subset of s of these landmarks. The remaining n-s landmarks are matched fully automatically by a thin-plate-spline based system using the s manual landmark correspondences to model the relationship between the scans. The method is applied to 47 pairs of temporal thoracic CT scans, three pairs of brain MR scans and five thoracic CT datasets with synthetic deformations. Interobserver differences are used to demonstrate the accuracy of the matched points. The utility of the reference standard data as a tool in evaluating registration is shown by the comparison of six sets of registration results on the 47 pairs of thoracic CT data.

SEGMENTATION OF THE PROSTATE IN MR IMAGES BY ATLAS MATCHING

Prostate cancer treatment by radiation therapy requires an accurate localisation of the prostate. For the treatment planning, primarily computed tomography (CT) images are used, but increasingly magnetic resonance (MR) images are added, because of their soft-tissue contrast. In current practice at our hospital, a manual delineation of the prostate is made, based on the CT and MR scans, which is a labour-intensive task. We propose an automatic segmentation method, based on non-rigid registration of a set of prelabelled MR atlas images. The algorithm consists of three stages. Firstly, the target image is nonrigidly registered with each atlas image, using mutual information as the similarity measure. After that, the best registered atlas images are selected by comparing the mutual information values after registration. Finally, the segmentation is obtained by averaging the selected deformed segmentations and thresholding the result. The method is evaluated on 22 images by calculating the overlap of automatic and manual segmentations. This results in a median Dice similarity coefficient of 0.82

Detecting mitotic figures in breast cancer histopathology images

The scoring of mitotic figures is an integrated part of the Bloom and Richardson system for grading of invasive breast cancer. It is routinely done by pathologists by visual examination of hematoxylin and eosin (H&E) stained histology slides on a standard light microscope. As such, it is a tedious process prone to inter- and intra-observer variability. In the last decade, whole-slide imaging (WSI) has emerged as the “digital age” alternative to the classical microscope. The increasing acceptance of WSI in pathology labs has brought an interest in the application of automatic image analysis methods, with the goal of reducing or completely eliminating manual input to the analysis. In this paper, we present a method for automatic detection of mitotic figures in breast cancer histopathology images. The proposed method consists of two main components: candidate extraction and candidate classification. Candidate objects are extracted by image segmentation with the Chan-Vese level set method. The candidate classification component aims at classifying all extracted candidates as being a mitotic figure or a false object. A statistical classifier is trained with a number of features that describe the size, shape, color and texture of the candidate objects. The proposed detection procedure was developed using a set of 18 whole-slide images, with over 900 manually annotated mitotic figures, split into independent training and testing sets. The overall true positive rate on the testing set was 59.5% while achieving 4.2 false positives per one high power field (HPF).

Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer

Numerous studies have shown the prognostic significance of nuclear morphometry in breast cancer patients. Wide acceptance of morphometric methods has, however, been hampered by the tedious and time consuming nature of the manual segmentation of nuclei and the lack of equipment for high throughput digitization of slides. Recently, whole slide imaging became more affordable and widely available, making fully digital pathology archives feasible. In this study, we employ an automatic nuclei segmentation algorithm to extract nuclear morphometry features related to size and we analyze their prognostic value in male breast cancer. The study population comprised 101 male breast cancer patients for whom survival data was available (median follow-up of 5.7 years). Automatic segmentation was performed on digitized tissue microarray slides, and for each patient, the mean nuclear area and the standard deviation of the nuclear area were calculated. In univariate survival analysis, a significant difference was found between patients with low and high mean nuclear area (P=0.022), while nuclear atypia score did not provide prognostic value. In Cox regression, mean nuclear area had independent additional prognostic value (P=0.032) to tumor size and tubule formation. In conclusion, we present an automatic method for nuclear morphometry and its application in male breast cancer prognosis. The automatically extracted mean nuclear area proved to be a significant prognostic indicator. With the increasing availability of slide scanning equipment in pathology labs, these kinds of quantitative approaches can be easily integrated in the workflow of routine pathology practice.

Registration of structurally dissimilar images in MRI-based brachytherapy.

A serious challenge in image registration is the accurate alignment of two images in which a certain structure is present in only one of the two. Such topological changes are problematic for conventional non-rigid registration algorithms. We propose to incorporate in a conventional free-form registration framework a geometrical penalty term that minimizes the volume of the missing structure in one image. We demonstrate our method on cervical MR images for brachytherapy. The intrapatient registration problem involves one image in which a therapy applicator is present and one in which it is not. By including the penalty term, a substantial improvement in the surface distance to the gold standard anatomical position and the residual volume of the applicator void are obtained. Registration of neighboring structures, i.e. the rectum and the bladder is generally improved as well, albeit to a lesser degree.

Registration of organs with sliding interfaces and changing topologies

Smoothness and continuity assumptions on the deformation field in deformable image registration do not hold for applications where the imaged objects have sliding interfaces. Recent extensions to deformable image registration that accommodate for sliding motion of organs are limited to sliding motion along approximately planar surfaces or cannot model sliding that changes the topological configuration in case of multiple organs. We propose a new extension to free-form image registration that is not limited in this way. Our method uses a transformation model that consists of uniform B-spline transformations for each organ region separately, which is based on segmentation of one image. Since this model can create overlapping regions or gaps between regions, we introduce a penalty term that minimizes this undesired effect. The penalty term acts on the surfaces of the organ regions and is optimized simultaneously with the image similarity. To evaluate our method registrations were performed on publicly available inhale-exhale CT scans for which performances of other methods are known. Target registration errors are computed on dense landmark sets that are available with these datasets. On these data our method outperforms the other methods in terms of target registration error and, where applicable, also in terms of overlap and gap volumes. The approximation of the other methods of sliding motion along planar surfaces is reasonably well suited for the motion present in the lung data. The ability of our method to handle sliding along curved boundaries and for changing region topology configurations was demonstrated on synthetic images.

Registration of multi-view apical 3D echocardiography images

Real-time three-dimensional echocardiography (RT3DE) is a non-invasive method to visualize the heart. Disadvantageously, it suffers from non-uniform image quality and a limited field of view. Image quality can be improved by fusion of multiple echocardiography images. Successful registration of the images is essential for prosperous fusion. Therefore, this study examines the performance of different methods for intrasubject registration of multi-view apical RT3DE images. A total of 14 data sets was annotated by two observers who indicated the position of the apex and four points on the mitral valve ring. These annotations were used to evaluate registration. Multi-view end-diastolic (ED) as well as end-systolic (ES) images were rigidly registered in a multi-resolution strategy. The performance of single-frame and multi-frame registration was examined. Multi-frame registration optimizes the metric for several time frames simultaneously. Furthermore, the suitability of mutual information (MI) as similarity measure was compared to normalized cross-correlation (NCC). For initialization of the registration, a transformation that describes the probe movement was obtained by manually registering five representative data sets. It was found that multi-frame registration can improve registration results with respect to single-frame registration. Additionally, NCC outperformed MI as similarity measure. If NCC was optimized in a multi-frame registration strategy including ED and ES time frames, the performance of the automatic method was comparable to that of manual registration. In conclusion, automatic registration of RT3DE images performs as good as manual registration. As registration precedes image fusion, this method can contribute to improved quality of echocardiography images.

Local atlas selection and performance estimation in multi-atlas based segmentation

In multi-atlas based segmentation, a new image is segmented by registering multiple atlas images and propagating the corresponding atlas segmentations. These propagated segmentations are then combined in a process called label fusion. This paper presents a new, local method that divides the propagated segmentations in multiple, user-definable regions. A label fusion process can then be applied to each of these regions separately and the end result can be constructed out of multiple partial results. The new method is compared to non-local label fusion methods, as well as with another local method called ALMAS. It is shown that local selection does not lead to a significant improvement in cases where existing methods already have a good result, but that our method significantly improves the result of atlas-based segmentation in cases where existing methods are less successful.