Keypoint Transfer for Fast Whole-Body Segmentation

We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfer label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ segmentations. We report segmentation results for abdominal organs in whole-body CT and MRI, as well as in contrast-enhanced CT and MRI. Our method offers a speed-up of about three orders of magnitude in comparison to common multi-atlas segmentation, while achieving an accuracy that compares favorably. Moreover, keypoint transfer does not require the registration to an atlas or a training phase. Finally, the method allows for the segmentation of scans with highly variable field-of-view.Comment: Accepted for publication at IEEE Transactions on Medical Imagin

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

[EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of 0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; Díaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). 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Patch-based segmentation with spatial context for medical image analysis

Accurate segmentations in medical imaging form a crucial role in many applications from pa- tient diagnosis to population studies. As the amount of data generated from medical images increases, the ability to perform this task without human intervention becomes ever more de- sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from images which have already been manually labelled by clinical experts. Methods using this ap- proach have been shown to be e ective in many applications, demonstrating great potential for automatic labelling of large datasets. However, these methods usually require the use of image registration and are dependent on the outcome of the registration. Any registrations errors that occur are also propagated to the segmentation process and are likely to have an adverse e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a relaxation of the required image alignment, whilst achieving similar results. In general, these methods label each voxel of a target image by comparing the image patch centred on the voxel with neighbouring patches from an atlas library and assigning the most likely label according to the closest matches. The main contributions of this thesis focuses around this approach in providing accurate segmentation results whilst minimising the dependency on registration quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework, which utilises both intensity and spatial information, and explore the use of spatial context in a diverse range of applications. The proposed methods extend the potential for patch-based segmentation to tolerate registration errors by rede ning the \locality" for patch selection and comparison, whilst also allowing similar looking patches from di erent anatomical structures to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging from the brain to the knees, demonstrating its potential with results which are competitive to state-of-the-art techniques.Open Acces

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin