BRISC—An Open Source Pulmonary Nodule Image Retrieval Framework

We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules. When presented with a nodule image, the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium (LIDC). The system (1) extracts images of individual nodules from the LIDC collection based on LIDC expert annotations, (2) stores the extracted data in a flat XML database, (3) calculates a set of quantitative descriptors for each nodule that provide a high-level characterization of its texture, and (4) uses various measures to determine the similarity of two nodules and perform queries on a selected query nodule. Using our framework, we compared three feature extraction methods: Haralick co-occurrence, Gabor filters, and Markov random fields. Gabor and Markov descriptors perform better at retrieving similar nodules than do Haralick co-occurrence techniques, with best retrieval precisions in excess of 88%. Because the software we have developed and the reference images are both open source and publicly available they may be incorporated into both commercial and academic imaging workstations and extended by others in their research

A single-center, observational study of 607 children & young people presenting with Differences in Sex Development (DSD)

Context Differences in sex development (DSD) represent a wide range of conditions presenting at different ages to various health professionals. Establishing a diagnosis, supporting the family and developing a management plan are important. Objective We aimed to better understand the presentation and prevalence of pediatric DSD. Design A retrospective, observational cohort study was undertaken of all children and young people (CYP) referred to a DSD multi-disciplinary team over 25 years (1995-2019). Setting A single tertiary paediatric center. Participants In total, 607 CYP (520 regional referrals) were included. Main Outcome Measures Data were analyzed for diagnosis, sex-assignment, age and mode of presentation, additional phenotypic features, mortality, and approximate point prevalence. Results Amongst the three major DSD categories, sex chromosome DSD was diagnosed in 11.2% (68/607) (most commonly 45, X/46, XY mosaicism), 46, XY DSD in 61.1% (371/607) (multiple diagnoses often with associated features), while 46, XX DSD occurred in 27.7% (168/607) (often 21-hydroxylase deficiency). Most children (80.1%) presented as neonates, usually with atypical genitalia, adrenal insufficiency, undescended testes or herniae. Those presenting later had diverse features. Rarely, the diagnosis was made antenatally (3.8%, n = 23) or following incidental karyotyping/family history (n = 14). Mortality was surprisingly high in 46, XY children, usually due to complex associated features (46, XY girls, 8.3%; 46, XY boys, 2.7%). The approximate point prevalence of neonatal referrals for investigation of DSD was 1 in 6,347 births, and 1 in 5,101 overall throughout childhood. Conclusions DSD represent a diverse range of conditions that can present at different ages. Pathways for expert diagnosis and management are important to optimize care

Graph-based topic models for trajectory clustering in crowd videos

Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph (STG) to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling