3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results

Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-Based Edge Detection

Abstract. Collimation is widely used for X-ray examinations to reduce the overall radiation exposure to the patient and improve the contrast resolution in the region of interest (ROI), that has been exposed directly to X-rays. It is desirable to detect the region of interest and exclude the unexposed area to optimize the image display. Although we only focus on the X-ray images generated with a rectangular collimator, it remains a challenging task because of the large variability of collimated images. In this study, we detect the region of interest as an optimal quadrilateral, which is the intersection of the optimal group of four half-planes. Each half-plane is defined as the positive side of a directed straight line. We develop an extended Hough transform for directed straight lines on a model-aware gray level edge-map, which is estimated with random forests [1] on features of pairs of superpixels. Experiments show that our algorithm can extract the region of interest quickly and accurately, despite variations in size, shape and orientation, and incompleteness of boundaries

Neural net based image matching

The paper describes a neural-based method for matching spatially distorted image sets. The matching of partially overlapping images is important in many applications - integrating information from images formed from different spectral ranges, detecting changes in a scene and identifying objects of differing orientations and sizes. Our approach consists of extracting contour features from both images, describing the contour curves as sets of line segments, comparing these sets, determining the corresponding curves and their common reference points, calculating the image-to-image co-ordinate transformation parameters on the basis of the most successful variant of the derived curve relationships. The main steps are performed by custom neural networks. The algorithms described in this paper have been successfully tested on a large set of images of the same terrain taken in different spectral ranges, at different seasons and rotated by various angles. In general, this experimental verification indicates that the proposed method for image fusion allows the robust detection of similar objects in noisy, distorted scenes where traditional approaches often fail

Neural net based image matching

The paper describes a neural-based method for matching spatially distorted image sets. The matching of partially overlapping images is important in many applications - integrating information from images formed from different spectral ranges, detecting changes in a scene and identifying objects of differing orientations and sizes. Our approach consists of extracting contour features from both images, describing the contour curves as sets of line segments, comparing these sets, determining the corresponding curves and their common reference points, calculating the image-to-image co-ordinate transformation parameters on the basis of the most successful variant of the derived curve relationships. The main steps are performed by custom neural networks. The algorithms described in this paper have been successfully tested on a large set of images of the same terrain taken in different spectral ranges, at different seasons and rotated by various angles. In general, this experimental verification indicates that the proposed method for image fusion allows the robust detection of similar objects in noisy, distorted scenes where traditional approaches often fail.</p

102 Feature Selection for Computer-Aided Polyp Detection using Genetic Algorithms

To improve computer aided diagnosis (CAD) for CT colonography we designed a hybrid classification scheme that uses a committee of support vector machines (SVMs) combined with a genetic algorithm (GA) for variable selection. The genetic algorithm selects subsets of four features, which are later combined to form a committee, with majority vote for classification across the base classifiers. Cross validation was used to predict the accuracy (sensitivity, specificity, and combined accuracy) of each base classifier SVM. As a comparison for GA, we analyzed a popular approach to feature selection called forward stepwise search (FSS). We conclude that genetic algorithms are effective in comparison to the forward search procedure when used in conjunction with a committee of support vector machine classifiers for the purpose of colonic polyp identification. Key Words: genetic algorithms, support vector machines, feature selection, forward stepwise search, computer aided diagnosis, virtual colonoscopy

A human observer study for evaluation and optimization of reconstruction methods in breast tomosynthesis using clinical cases

In breast tomosynthesis1 (BT) a number of 2D projection images are acquired from different angles along a limited arc. The imaged breast volume is reconstructed from the projection images, providing 3D information. The purpose of the study was to investigate and optimize different reconstruction methods for BT in terms of image quality using human observers viewing clinical cases. Sixty-six cases with suspected masses and calcifications were collected from 55 patients. Four different reconstructions of each image set were evaluated by four observers (two experienced radiologists, two experienced medical physicists): filtered back projection (FBP), iterative adapted FBP (iFBP) and two ML-convex iterative algorithm (MLCI) reconstructions (8 and 10 iterations) that differed in noise level and contrast of clinical details. Representation of masses and microcalcifications was evaluated. The structures were rated according to the overall appearance in a rank-order study. The differently reconstructed images of the same structure were displayed side by side in random order. The observers were forced to rank the order of the different reconstructed images and their proportions at each rank were scored. The results suggest that even though the FBP contains most noise its reconstructions are considered best overall, followed by iFBP, which contains least noise. In both FBP and iFBP methods the sharp borders and mass speculations were better represented than in iterative reconstructions while out-of-plane artifacts were better suppressed in the latter. However, in clinical practice the differences between the reconstructions may be considered negligible