Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P.
Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015;9351 :80-8.
AbstractWe present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.
Lou Y, Tannenbaum A.
Inter-modality Deformable Registration. In: Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press ; 2015.
AbstractDeformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Lou Y, Tannenbaum A.
Inter-modality Deformable Registration. In: Jia, X., and Jiang, S.B. (Eds.). (2015). Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Vol. Ch 10. CRC Press ; 2015.
AbstractDeformable image registration (DIR) is one of the major problems in medical image processing, such as dose calculation [18], treatment planning [33] and scatter removal of cone beam CT (CBCT) [22]. It is of prime importance to establish a pixel-to-pixel correspondence between two images in many clinical scenarios. For instance, registration of a CT image to MRI of a patient taken at different time can provide complementary diagnostic information. For applications as such, since the deformation of the patient anatomy cannot be represented by a rigid transform, DIR is almost the sole means to establish this mapping. DIR can be generally categorized into intra-modality and inter-modality, or multi-modality. While intra-modality DIR can be easily handled by conventional intensity-based methods [11, 30], intermodality DIR problems are still far from being satisfactory. Yet, since different imaging modalities usually provide their unique angles to reveal patient anatomy and delineate microscopic disease, intermodality registration plays a key role to combine the information from multiple modalities to facilitate diagnostics and treatment of a certain disease.
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng DD.
Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis. Artificial Life and Computational Intelligence. 2015;LNAI 8955 :350-9.
AbstractFeature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the highdimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks.
Liu ACALCI 2015 Klein T, Wells III WM.
RF Ultrasound Distribution-Based Confidence Maps. Int Conf Med Image Comput Comput Assist Interv. 2015;18 (Pt2) :595-602.
AbstractUltrasound is becoming an ever increasingly important modality in medical care. However, underlying physical acquisition principles are prone to image artifacts and result in overall quality variation. Therefore processing medical ultrasound data remains a challenging task. We propose a novel distribution-based measure of assessing the confidence in the signal, which emphasizes uncertainty in attenuated as well as shadow regions. In contrast to the similar recently proposed method that relies on image intensities, the new approach makes use of the enveloped-detected radio-frequency data, facilitating the use of Nakagami speckle statistics. Employing J-divergence as distance measure for the random-walk based algorithm, provides a natural measure of similarity, yielding a more reliable estimate of confidence. For evaluation of the model’s performance, tests are conducted on the application of shadow detection. Additionally, computed maps are presented for different organs such as neck, liver and prostate, showcasing the properties of the model. The probabilistic approach is shown to have beneficial features for image processing tasks.
Klein MICCAI 2015