Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins

Deploying off-the-shelf segmentation networks on biomedical data has become common practice, yet if structures of interest in an image sequence are visible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segmentation masks from frames with low uncertainty to those frames with high uncertainty to handle temporary loss of signal for segmentation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney (HEK293T) cells transiently transfected with a fluorescent protein that moves in and out of the nucleus over time. The method presented here will empower microscopic experiments aimed at understanding molecular and cellular function.Comment: Accepted at MICCAI Workshop on Medical Image Learning with Less Labels and Imperfect Data, 202

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results

A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation

When introducing advanced image computing algorithms, e.g., whole-heart segmentation, into clinical practice, a common suspicion is how reliable the automatically computed results are. In fact, it is important to find out the failure cases and identify the misclassified pixels so that they can be excluded or corrected for the subsequent analysis or diagnosis. However, it is not a trivial problem to predict the errors in a segmentation mask when ground truth (usually annotated by experts) is absent. In this work, we attempt to address the pixel-wise error map prediction problem and the per-case mask quality assessment problem using a unified deep learning (DL) framework. Specifically, we first formalize an error map prediction problem, then we convert it to a segmentation problem and build a DL network to tackle it. We also derive a quality indicator (QI) from a predicted error map to measure the overall quality of a segmentation mask. To evaluate the proposed framework, we perform extensive experiments on a public whole-heart segmentation dataset, i.e., MICCAI 2017 MMWHS. By 5-fold cross validation, we obtain an overall Dice score of 0.626 for the error map prediction task, and observe a high Pearson correlation coefficient (PCC) of 0.972 between QI and the actual segmentation accuracy (Acc), as well as a low mean absolute error (MAE) of 0.0048 between them, which evidences the efficacy of our method in both error map prediction and quality assessment.Comment: 9 pages, accepted by MICCAI'1

Respuesta de hipersensibilidad retardada en pacientes candidatos a artroplastia de cadera

Se estudiaron 100 pacientes escogidos de manera aleatoria de entre los candidatos a artroplastia de cadera (47 por fractura subcapital de fémur Garden IV, y 53 por coxartrosis). En el momento del ingresos se determinaron en sangre niveles de albúmina, proteínas totales y hemoglobina; y se midió la respuesta de hipersensibilidad retardada mediante la inoculación de siete antígenos de memoria con un aplicador Multitest® (Mérieux). La lectura de la reacción de induración se realizó a las 48 horas. Los pacientes fueron clasificados en: normoérgicos (+) a dos o más antígenos) y anérgicos (una o ninguna respuesta (+). La población normoérgica representó el 61% de los pacientes, con una edad 12,5 años menor que la población anérgica (p<0,001). Los pacientes normoérgicos presentaron cifras significativamente más altas de hemoglobina (p<0,001), proteínas totales (p<0,05) y albúmina (p<0,001). La situación de anergia fue más frecuente en el grupo de mujeres (29/57) frente al de hombres (10/43; p<0,001), y en el grupo de fracturas subcapitales (31/47) que en el de coxartrosis (8/53) (p<0,001). No se hallaron relaciones estadísticamente significativas entre los distintos parámetros estudiados y la aparición de infección en el postoperatorio. Las diferencias aparecidas reflejan alteraciones en la respuesta inmunológica que, sin embargo, no resultan pronósticas frente a la aparición de infección en nuestra serie.One hundred candidates for hip arthroplasty were chosen at random (47 Garden IV femoral neck fractures, and 53 osteoarthrosis). Before surgery, serum albumin, total proteins and haemoglobin were determined in peripheral blood. All patients were skin tested with seven memory antigens (Multitest®, Mérieux) in order to measure the delayed hypersensitivity response. The diameter of the resulting induration was measured 48 hours after injection. Patients were classified as reactive if they responded to one antigen or showed no response. Reactive patients supposed 61% of total, and anergic patients were 12,5 years older (p<0,001). Levels of haemoglobin (p<0,001), total proteins (p<0,05) and albumin (p<0,001) were higher in reactive patients. Anergy was more frequent in women (29/57) than in men (10/43; p<0,001), and in fractures (31/47) than in osteoarthrosis (8/53) (p<0,001). No association between the variables studied and postoperative infection was found. These differences show immunologic alterations. However, they have no prognostic value for postoperative infection in hip arthroplasty patients

Distribution of Breeding Shorebirds on the Arctic Coastal Plain of Alaska

Available information on the distribution of breeding shorebirds across the Arctic Coastal Plain of Alaska is dated, fragmented, and limited in scope. Herein, we describe the distribution of 19 shorebird species from data gathered at 407 study plots between 1998 and 2004. This information was collected using a single-visit rapid area search technique during territory establishment and early incubation periods, a time when social displays and vocalizations make the birds highly detectable. We describe the presence or absence of each species, as well as overall numbers of species, providing a regional perspective on shorebird distribution. We compare and contrast our shorebird distribution maps to those of prior studies and describe prominent patterns of shorebird distribution. Our examination of how shorebird distribution and numbers of species varied both latitudinally and longitudinally across the Arctic Coastal Plain of Alaska indicated that most shorebird species occur more frequently in the Beaufort Coastal Plain ecoregion (i.e., closer to the coast) than in the Brooks Foothills ecoregion (i.e., farther inland). Furthermore, the occurrence of several species indicated substantial longitudinal directionality. Species richness at surveyed sites was highest in the western portion of the Beaufort Coastal Plain ecoregion. The broad-scale distribution information we present here is valuable for evaluating potential effects of human development and climate change on Arctic-breeding shorebird populations.Les renseignements qui existent en matière de répartition des oiseaux de rivage en reproduction sur la plaine côtière de l’Arctique en Alaska sont anciens, fragmentés et restreints. Ici, nous décrivons la répartition de 19 espèces d’oiseaux de rivage à partir de données recueillies à 407 lieux de recherche entre 1998 et 2004. Cette information a été recueillie grâce à une technique de recherche consistant en une seule visite rapide durant les périodes d’établissement du territoire et de début d’incubation, périodes pendant lesquelles les comportements sociaux et les vocalisations permettent de bien repérer les oiseaux. Nous décrivons la présence ou l’absence de chaque espèce, de même que le nombre général d’espèces, ce qui procure une perspective régionale de la répartition des oiseaux de rivage. Nous comparons et contrastons nos cartes de répartition des oiseaux de rivage à celles d’études antérieures, en plus de décrire les tendances les plus marquées en matière de répartition des oiseaux de rivage. Notre examen de la variation latitudinale et longitudinale en matière de répartition et de nombre d’espèces d’oiseaux de rivage à l’échelle de la plaine côtière arctique de l’Alaska nous a permis de constater que la plupart des espèces d’oiseaux de rivage se manifestaient plus souvent dans la région écologique de la plaine côtière de Beaufort (c’est-à-dire plus proche de la côte) que dans la région écologique des contreforts de Brooks (c’est-à-dire plus à l’intérieur des terres). Par ailleurs, l’occurrence de plusieurs espèces indiquait une directionalité longitudinale substantielle. La richesse des espèces aux sites à l’étude était à son meilleur dans la partie ouest de la région écologique de la plaine côtière de Beaufort. Les renseignements sur la répartition à grande échelle que nous présentons ici jouent un rôle dans l’évaluation des effets éventuels des travaux de mise en valeur par l’être humain et du changement climatique sur les populations d’oiseaux de rivage en reproduction de l’Arctique

Revisión sistemática: Evaluación de la adherencia del tratamiento de pacientes naive con hepatitis C.

Introducción: El VHC en los seres humanos puede causar diferentes enfermedades hepáticas. La característica más significativa de la enfermedad es su alta tendencia a cronificarse. Objetivo: Realizar una revisión sistemática de estudios que evalúan la adherencia en pacientes con hepatitis C tratados con PEG-IFN/ RBV y si una mejor adherencia hace conseguir un mayor número de pacientes que obtengan una RVS. Métodos: Los datos se obtuvieron mediante la búsqueda en Pubmed de revisiones sistemáticas en inglés publicadas en los últimos 5 años, empleando como palabras claves: “VHC treatment adherence”, “Sustained Virologic Response”. Se seleccionaron los estudios que evalúan la adherencia al  tratamiento antiviral en pacientes sin coinfección con otra viremia. En una segunda búsqueda se utilizaron “telaprevir and boceprevir review”. Resultados: En especial, en los pacientes con genotipo 1, una buena adherencia aumenta significativamente RVS (63% vs 34%). Las tasas de RVS con los nuevos antivirales han logrado aumentar en relación con la terapia dual hasta un 70% en naives, 30% en no respondedores y 80% recurrentes. Conclusión: Los pacientes con genotipo 1 deben mantener una mejor adherencia que pacientes con genotipo no 1. La interrupción del tratamiento, falta de adherencia por pérdidas de dosis de PEG-INF/RBV y los efectos adversos son los principales obstáculos para alcanzar la RVS

Revisión sistemática: Evaluación de la adherencia del tratamiento de pacientes naive con hepatitis C.

Introducción: El VHC en los seres humanos puede causar diferentes enfermedades hepáticas. La característica más significativa de la enfermedad es su alta tendencia a cronificarse. Objetivo: Realizar una revisión sistemática de estudios que evalúan la adherencia en pacientes con hepatitis C tratados con PEG-IFN/ RBV y si una mejor adherencia hace conseguir un mayor número de pacientes que obtengan una RVS. Métodos: Los datos se obtuvieron mediante la búsqueda en Pubmed de revisiones sistemáticas en inglés publicadas en los últimos 5 años, empleando como palabras claves: “VHC treatment adherence”, “Sustained Virologic Response”. Se seleccionaron los estudios que evalúan la adherencia al  tratamiento antiviral en pacientes sin coinfección con otra viremia. En una segunda búsqueda se utilizaron “telaprevir and boceprevir review”. Resultados: En especial, en los pacientes con genotipo 1, una buena adherencia aumenta significativamente RVS (63% vs 34%). Las tasas de RVS con los nuevos antivirales han logrado aumentar en relación con la terapia dual hasta un 70% en naives, 30% en no respondedores y 80% recurrentes. Conclusión: Los pacientes con genotipo 1 deben mantener una mejor adherencia que pacientes con genotipo no 1. La interrupción del tratamiento, falta de adherencia por pérdidas de dosis de PEG-INF/RBV y los efectos adversos son los principales obstáculos para alcanzar la RVS

stella Is a Maternal Effect Gene Required for Normal Early Development in Mice

Abstractstella is a novel gene specifically expressed in primordial germ cells, oocytes, preimplantation embryos, and pluripotent cells [1, 2]. It encodes a protein with a SAP-like domain [3] and a splicing factor motif-like structure, suggesting possible roles in chromosomal organization or RNA processing. Here, we have investigated the effects of a targeted mutation of stella in mice. We show that while matings between heterozygous animals resulted in the birth of apparently normal stella null offspring, stella-deficient females displayed severely reduced fertility due to a lack of maternally inherited Stella-protein in their oocytes. Indeed, we demonstrate that embryos without Stella are compromised in preimplantation development and rarely reach the blastocyst stage. stella is thus one of few known mammalian maternal effect genes [4–9], as the phenotypic effect on embryonic development is mainly a consequence of the maternal stella mutant genotype. Furthermore, we show that STELLA that is expressed in human oocytes [10] is also expressed in human pluripotent cells and in germ cell tumors. Interestingly, human chromosome 12p, which harbours STELLA, is consistently overrepresented in these tumors [11]. These findings suggest a similar role for STELLA during early human development as in mice and a potential involvement in germ cell tumors