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Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Abstract. Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high-and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.

This chapter presents a comparative study of texture classification in computed tomography images of the human lungs. Popular texture descriptors used in the medical image analysis literature for texture-based emphysema classification are described and evaluated within the same classification framework. Further, it is investigated whether combining the different descriptors is beneficial.

Introduction At present, no disease-modifying osteoarthritis drugs (DMOADS) are approved by the FDA (US Food and Drug Administration); possibly partly due to inadequate trial design since efficacy demonstration requires disease progression in the placebo group. We investigated whether combinations of biochemical and magnetic resonance imaging (MRI)-based markers provided effective diagnostic and prognostic tools for identifying subjects with high risk of progression. Specifically, we investigated aggregate cartilage longevity markers combining markers of breakdown, quantity, and quality.

To develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-shapes and study metrics on the shape space. This shape space has singularities which correspond to topological transitions in the represented trees. We study two closely related metrics on the shape space, TED and QED. QED is a quotient euclidean distance arising naturally from the shape space formulation, while TED is the classical tree edit distance. Using Gromov's metric geometry, we gain new insight into the geometries defined by TED and QED. We show that the new metric QED has nice geometric properties that are needed for statistical analysis: Geodesics always exist and are generically locally unique. Following this, we can also show the existence and generic local uniqueness of average trees for QED. TED, while having some algorithmic advantages, does not share these advantages. Along with the theoretical framework we provide experimental proof-of-concept results on synthetic data trees as well as small airway trees from pulmonary CT scans. This way, we illustrate that our framework has promising theoretical and qualitative properties necessary to build a theory of statistical tree-shape analysis.

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Magnesium and sulfate are each known to affect calcite growth and dissolution, but little is known about their combined effects on calcite growth rates. We grew calcite using the constant composition approach at ambient conditions, monitoring inhibition in solutions of Mg 2+ and SO 4 2− individually and together. The growth rate for pure calcite averaged 4.35 × 10 −6 mol m −2 s −1 but decreased to 0.34, 0.16, and 0.08 × 10 −6 mol m −2 s −1 in solutions with 40 mM of SO 4 2− , 13.3 mM of Mg 2+ , and 12.7 mM of MgSO 4 . We characterized the crystal form with scanning electron microscopy and atomic force microscopy. The {101̅ 0} crystal surface developed as the foreign ion concentration increased in the order SO 4 2− < Mg 2+ < MgSO 4 . Powder X-ray diffraction and X-ray photoelectron spectroscopy showed Mg incorporation of as much as 9.2 mol %. Mg 2+ inhibits calcite growth more effectively when SO 4 2− is also present, which we interpret to be the result of MgSO 4 ion pair formation. Sulfate promotes Mg 2+ dehydration, thereby allowing calcite uptake at lower temperatures. These results improve general understanding about the controls on biomineralisation and imply a need for re-examining the validity of the Mg/Ca thermometer, which uses the Mg composition in foraminifer for interpreting ancient seawater temperatures.