Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Effects of neuropeptide Y, insulin, 2-deoxyglucose, and food deprivation on food-motivated behavior

The current study demonstrates the ability of neuropeptide Y (NPY) to increase break points under a progressive ratio 1 (PR1) reinforcement schedule. An initial response resulted in delivery of a food reinforcer (45 mg pellet) under the PR1, and an additional response was required for each successive reinforcer. The break point, the number of responses emitted to obtain the last reinforcer, is considered a measure of reinforcing efficacy or motivational strength of the food reinforcer. NPY (0.3–10 µg) significantly increased break point to levels comparable to those produced by 36–48 h of food deprivation. Although insulin (3–8 U/kg) and 2-deoxyglucose (150–250 mg/kg) also increased food intake, neither increased break points to levels produced by NPY or food deprivation. These data suggest that NPY may change the value of food in ways that cannot be accounted for by changes in insulin, glucose levels or intracellular glucoprivation. These results emphasize that simply measuring the amount of freely available food eaten is not a fully adequate measure of the strength of the feeding behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46350/1/213_2006_Article_BF02311173.pd

d̅ and 3He̅ Production in √sNN = 130 GeV Au+Au Collisions

A report on the first measurements of light antinucleus production in Au + Au collisions at the Relativistic Heavy-Ion Collider (RHIC) was presented. The production rates for d̄ and He were observed to be much larger than in lower energy nucleus-nucleus collisions. A little or no increase in the antinucleon freeze-out volume compared to CERN Super Proton Synchrotron (SPS) energy was indicated by a coalescence model analysis. The He freeze-out volume was indicated to be smaller than the d̄ freeze-out volume

Pion interferometry of root s(NN)=130 GeV Au+Au collisions at RHIC

Two-pion correlation functions in An + Au collisions at roots(NN) = 130 GeV have been measured by the STAR (solenoidal tracker at RHIC) detector. The source size extracted by fitting the correlations grows with event multiplicity and decreases with transverse momentum. Anomalously large sizes or emission durations, which have been suggested as signals of quark-gluon plasma formation and rehadronization, are not observed. The Hanbury Brown-Twiss parameters display a weak energy dependence over a broad range in roots(NN)

Segmentierung von zervikalen Lymphknoten in T1-gewichteten MRT-Aufnahmen

Die Untersuchung von Größe und Aussehen eines Lymphknotens kann ein entscheidender Indikator für die Existenz eines Tumors sein und ist außerdem ein probates Mittel, um Verlaufsanalysen bei einem Patienten durchzuführen, welche wiederum maßgeblichen Einfluss auf die Behandlung haben können. Um die Größe und andere Parameter des Lymphknotens bestimmen zu können, ist zuerst eine Segmentierung vonnöten.Wir präsentieren ein neues Verfahren für die halbautomatische Segmentierung von Lymphknoten auf MR-Datensätzen. Unser Ansatz verwendet eine Wasserscheidentransformation als Grundlage und kombiniert diese mit einem Radialstrahlbasierten Verfahren, um eine möglichst akurate Segmentierung des Lymphknotens zu erhalten. Für die Evaluation wurden 95 Lymphknoten-Segmentierungen aus 17 verschiedenen, kontrastverstärkten T1-gewichteten Patientendatensätzen verwendet. Das durchschnittliche Dice ¨ Ahnlichkeitsmaß lag bei 0.69±0.15 und die mittlere Oberflächendistanz bei 0.65±0.54mm