An Anatomy-based Iteratively Searching Convolutional Neural Network for Organ Localization in CT images

Organ localization is a common and essential preprocessing operation for many medical image analysis tasks. We propose a novel multi-organ localization method based on an end-to-end 3D convolutional neural network. The proposed algorithm employs a regression network to learn the position relationship between any patch and target organs in a medical computed tomography (CT) image. With this framework, it can iteratively localize the target organs in a coarse-to-fine manner. The main idea behind this method is to embed the anatomy of structures in a deep learning-based approach. For implementation, the proposed network outputs an 8-dimensional vector that contains information about the position, scale, and presence of each target organ. A piecewise loss function and a multi-density sampling strategy help to optimize this network to learn anatomy layout characteristics over the entire CT image. Starting from a random position, this network can accurately locate the target organ with a few iterations. Moreover, a dual-resolution strategy is employed to improve the accuracy affected by varying organ scales, further enhancing the localizing performance for all organs. We evaluate our method on a public data set (LiTS) to locate 11 organs in the thoraco-abdomino-pelvic region. The proposed method outperforms state-of-the-art methods with a mean intersection over union (IOU) of 80.84%, mean wall distance of 3.63 mm, and mean centroid distance of 4.93 mm, constituting excellent accuracy. The improvements on relatively small-size and medium-size organs are noteworthy.

Analysis of major paralogs encoding the Fra a 1 allergen based on their organ-specificity in Fragaria × ananassa

Fra a 1 protein in strawberry causes oral allergic syndrome. Over 39 Fra a 1 paralogs have been identified in strawberry genome. Fra a 1.01 is major accumulating protein in edible organs. Strawberry fruits contain allergenic proteins that cause oral allergic syndrome. The hypothesized major allergen is Fra a 1, an ortholog of the birch pollen allergen protein Bet v 1. We organized Fra a 1 genes and analyzed their localizations at the transcriptional and translational levels. In total, 15 new Fra a 1 proteins were identified from the genomic database, increasing the total number of Fra a 1 to 30 proteins encoded by 39 genes. Fra a 1.02 was mostly expressed in receptacles, and Fra a 1.01 in achenes, when analyzed by RNA sequencing. Immunoblotting showed that the Fra a 1.01 protein was broadly accumulated in strawberry organs, while the Fra a 1.02 protein was mostly expressed in receptacles. Recombinant Fra a 1.01 strongly reacted with human IgE. The mRNA and protein expression levels of Fra a 1 did not correlate, indicating the importance of protein levels when evaluating the abundance of allergens in strawberry. Based on the localizations, accumulation levels and reactivity to human IgE, we determined that Fra a 1.01 was the most important allergen, followed by Fra a 1.02, and then other Fra a 1 proteins. The information obtained here will be useful for selecting the target Fra a 1 paralogs when breeding hypoallergenic strawberry.

Evaluation of Body-Wise and Organ-Wise Registrations For Abdominal Organs

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.

Landmark constellation models for medical image content identification and localization

PURPOSE

Many medical imaging tasks require the detection and localization of anatomical landmarks, for example for the initialization of model-based segmentation or to detect anatomical regions present in an image. A large number of landmark and object localization methods have been described in the literature. The detection of single landmarks may be insufficient to achieve robust localization across a variety of imaging settings and subjects. Furthermore, methods like the generalized Hough transform yield the most likely location of an object, but not an indication whether or not the landmark was actually present in the image.

METHODS

For these reasons, we developed a simple and computationally efficient method combining localization results from multiple landmarks to achieve robust localization and to compute a localization confidence measure. For each anatomical region, we train a constellation model indicating the mean relative locations and location variability of a set of landmarks. This model is registered to the landmarks detected in a test image via point-based registration, using closed-form solutions. Three different outlier suppression schemes are compared, two using iterative re-weighting based on the residual landmark registration errors and the third being a variant of RANSAC. The mean weighted residual registration error serves as a confidence measure to distinguish true from false localization results. The method is optimized and evaluated on synthetic data, evaluating both the localization accuracy and the ability to classify good from bad registration results based on the residual registration error.

RESULTS

Two application examples are presented: the identification of the imaged anatomical region in trauma CT scans and the initialization of model-based segmentation for C-arm CT scans with different target regions. The identification of the target region with the presented method was in 96 % of the cases correct.

CONCLUSION

The presented method is a simple solution for combining multiple landmark localization results. With appropriate parameters, outlier suppression clearly improves the localization performance over model registration without outlier suppression. The optimum choice of method and parameters depends on the expected level of noise and outliers in the application at hand, as well as on the focus on localization, classification, or both. The method allows detecting and localizing anatomical fields of view in medical images and is well suited to support a wide range of applications comprising image content identification, anatomical navigation and visualization, or initializing the pose of organ shape models.

Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models

Parsing 2D radiographs into anatomical regions is a challenging task with many applications. In the clinic, scans routinely include anterior-posterior (AP) and lateral (LAT) view radiographs. Since these orthogonal views provide complementary anatomic information, an integrated analysis can afford the greatest localization accuracy. To solve this integration we propose automatic landmark candidate detection, pruned by a learned geometric consensus detector model and refined by fitting a hierarchical active appearance organ model (H-AAM). Our main contribution is twofold. First, we propose a probabilistic joint consensus detection model which learns how landmarks in either or both views predict landmark locations in a given view. Second, we refine landmarks by fitting a joint H-AAM that learns how landmark arrangement and image appearance can help predict across views. This increases accuracy and robustness to anatomic variation. All steps require just seconds to compute and compared to processing the scouts separately, joint processing reduces mean landmark distance error from 27.3 mm to 15.7 mm in LAT view and from 12.7 mm to 11.2 mm in the AP view. The errors are comparable to human expert inter-observer variability and suitable for clinical applications such as personalized scan planning for dose reduction. We assess our method using a database of scout CT scans from 93 subjects with widely varying pathology.