Anatomical Structures In Medical Images Research Articles

A novel online boosting algorithm for automatic anatomy detection

This paper presents a novel online learning method for automatically detecting anatomic structures in medical images. Conventional off-line learning methods require collecting a complete set of representative samples prior to training a detector. Once the detector is trained, its performance is fixed. To improve the performance, the detector must be completely retrained, demanding the maintenance of historical training samples. Our proposed online approach eliminates the need for storing historical training samples and is capable of continually improving performance with new samples. We evaluate our approach with three distinct thoracic structures, demonstrating that our approach yields performance competitive with the off-line approach. Furthermore, we investigate the properties of our proposed method in comparison with an online learning method suggested by Grabner and Bischof (IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006, vol. 1, pp. 260---267, 2006), which is the state of the art, indicating that our proposed method runs faster, offers more stability, improves handling of "catastrophic forgetting", and simultaneously achieves a satisfactory level of adaptability. The enhanced performance is attributed to our novel online learning structure coupled with more accurate weaker learners based on histograms.

A hybrid approach for segmentation of anatomic structures in medical images

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost. Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image. Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%. The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.

Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies

Automatic segmentation of anatomical structures in medical images is a valuable tool for efficient computer-aided radiotherapy and surgery planning and an enabling technology for dynamic adaptive radiotherapy. This paper presents the design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning. A coarse to fine approach is followed that consists of presegmentation, anatomic orientation and structure segmentation. No user input or a priori information about the image content is required. In presegmentation, the body outline, the bones and lung equivalent tissue are detected. Anatomic orientation recognizes the patient's position, orientation and gender and creates an elastic mapping of the slice positions to a reference scale. Structure segmentation is divided into localization, outlining and refinement, performed by procedures with implicit anatomic knowledge using standard image processing operations. The presented version of algorithms automatically segments the body outline and bones in any gender and patient position, the prostate, bladder and femoral heads for male pelvis in supine position, and the spinal canal, lungs, heart and trachea in supine position. The software was developed and tested on a collection of over 600 clinical radiotherapy planning CT stacks. In a qualitative validation on this test collection, anatomic orientation correctly detected gender, patient position and body region in 98% of the cases, a correct mapping was produced for 89% of thorax and 94% of pelvis cases. The average processing time for the entire segmentation of a CT stack was less than 1 min on a standard personal computer. Two independent retrospective studies were carried out for clinical validation. Study I was performed on 66 cases (30 pelvis, 36 thorax) with dosimetrists, study II on 52 cases (39 pelvis, 13 thorax) with radio-oncologists as experts. The experts rated the automatically produced structures on the scale 1—excellent (no corrections necessary, maximum time saving), 2—good (corrections necessary for up to 1/3 of slices), 3—acceptable (major corrections necessary, but still time saving), 4—not acceptable (manual redrawing more efficient, no time saving). A rating ⩽ 3 indicates a time saving in the treatment planning process and was given for pelvis segmentation in 70% (I) and 68% (II) of the cases, with average ratings 2.9 (I) and 2.6 (II). For the thorax, a rating ⩽ 3 was given in 94% and 91% of the cases, with average ratings 2.1 and 1.9, respectively. For quantitative validation, automatically generated structures were compared geometrically in 2D and 3D to manually drawn structures created by experts on seven randomly selected cases. The quantitative validation was limited to pelvis structures. The results indicate that the accuracy of the algorithms is within the bandwidth of manual segmentation by experts, except for specific erroneous situations. Even though manual review and corrections of automatically segmented structures are still mandatory, it can be expected that due to the speed of the presented software and the quality of its results, its introduction in the radiotherapy treatment planning process will lead to a considerable amount of time being saved.

Automatic atlas-based contour extraction of anatomical structures in medical images

We present an extension of the live-wire approach to an automatic segmentation method. Similarities between anatomical structures in different patient data are exploited by transferring structure outlines from atlas to patient data. After preprocessing via affine registration methods, the automatic segmentation is done slice by slice. Atlas seed points are relocated in each patient slice and then connected by cost-trained live-wires. After the automatic contour extraction, an efficient graph search algorithm facilitates the interactive adjustment of the contours created. The evaluation with hip CT data showed that the tool simplifies the segmentation of anatomical hip structures because 51% of interaction time could be saved maintaining the segmentation quality compared to manually created segmentations.

Deformable organisms for automatic medical image analysis

We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose ‘deformable organisms’, autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a ‘corpus callosum worm’ that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.

Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields

We present a new approach to shape-based segmentation and tracking of deformable anatomical structures in medical images, and validate this approach by detecting and tracking the endocardial contour in an echocardiographic image sequence. To this end, some global prior shape knowledge of the endocardial boundary is captured by a prototype template with a set of predefined global and local deformations to take into account its inherent natural variability over time. In this deformable model-based Bayesian segmentation, the data likelihood model relies on an accurate statistical modelling of the grey level distribution of each class present in the ultrasound image. The parameters of this distribution mixture are given by a preliminary iterative estimation step. This estimation scheme relies on a Markov Random Field prior model, and takes into account the imaging process as well as the distribution shape of each class present in the image. Then the detection and the tracking problem is stated in a Bayesian framework, where it ends up as a cost function minimisation problem for each image of the sequence. In our application, this energy optimisation problem is efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images, and on a real echocardiographic image sequence.

An active contour model algorithm for tracking endocardiac boundaries in echocardiographic sequences.

The use of active contour models to track the boundaries of anatomic structures in medical images is a technique that has attracted a great number of efforts during the last decade. Segmentation techniques based in deformable active contours were proposed first by Kass et al. Because of the problems appearing using these models, some solutions have been introduced, such as balloon force or Gradient Vector Flow force (GVF), derived from the Gradient Vector Flow vectorial field. Results obtained with these forces in the tracking endocardiac task in echocardiographic sequences were not adequate. We have designed a new external force called hybrid force, which, by combining both forces, joins the main features of each one.