2,500 research outputs found
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
Automatic parsing of human anatomies at instance-level from 3D computed
tomography (CT) scans is a prerequisite step for many clinical applications.
The presence of pathologies, broken structures or limited field-of-view (FOV)
all can make anatomy parsing algorithms vulnerable. In this work, we explore
how to exploit and conduct the prosperous detection-then-segmentation paradigm
in 3D medical data, and propose a steerable, robust, and efficient computing
framework for detection, identification, and segmentation of anatomies in CT
scans. Considering complicated shapes, sizes and orientations of anatomies,
without lose of generality, we present the nine degrees-of-freedom (9-DoF) pose
estimation solution in full 3D space using a novel single-stage,
non-hierarchical forward representation. Our whole framework is executed in a
steerable manner where any anatomy of interest can be directly retrieved to
further boost the inference efficiency. We have validated the proposed method
on three medical imaging parsing tasks of ribs, spine, and abdominal organs.
For rib parsing, CT scans have been annotated at the rib instance-level for
quantitative evaluation, similarly for spine vertebrae and abdominal organs.
Extensive experiments on 9-DoF box detection and rib instance segmentation
demonstrate the effectiveness of our framework (with the identification rate of
97.0% and the segmentation Dice score of 90.9%) in high efficiency, compared
favorably against several strong baselines (e.g., CenterNet, FCOS, and
nnU-Net). For spine identification and segmentation, our method achieves a new
state-of-the-art result on the public CTSpine1K dataset. Last, we report highly
competitive results in multi-organ segmentation at FLARE22 competition. Our
annotations, code and models will be made publicly available at:
https://github.com/alibaba-damo-academy/Med_Query.Comment: updated versio
Quantitative geometric analysis of rib, costal cartilage and sternum from childhood to teenagehood
Better understanding of the effects of growth on childrenâs bones and cartilage is necessary for clinical and biomechanical purposes. The aim of this study is to define the 3D geometry of childrenâs rib cages: including sternum, ribs and costal cartilage. Three-dimensional reconstructions of 960 ribs, 518 costal cartilages and 113 sternebrae were performed on thoracic CT-scans of 48 children, aged four months to 15 years. The geometry of the sternum was detailed and nine parameters were used to describe the ribs and rib cages. A "costal index" was defined as the ratio between cartilage length and whole rib length to evaluate the cartilage ratio for each rib level. For all children, the costal index decreased from rib level one to three and increased from level three to seven. For all levels, the cartilage accounted for 45 to 60% of the rib length, and was longer for the first years of life. The mean costal index decreased by 21% for subjects over three years old compared to those under three (p<10-4). The volume of the sternebrae was found to be highly age dependent. Such data could be useful to define the standard geometry of the paediatric thorax and help to detect clinical abnormalities.Grant from the ANR (SECUR_ENFANT 06_0385) and supported by the GDR 2610 âBiomĂ©canique des chocsâ (CNRS/INRETS/GIE PSA Renault
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Sensor, Signal, and Imaging Informatics in 2017.
ObjectiveâTo summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.MethodsâPubMedÂź and Web of ScienceÂź were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.ResultsâThe selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT)
scans remains in a great demand in the clinical environment because the
available methods fail to provide a generic solution due to wide anatomical
variations of lungs and existence of pathologies. Manual annotation, current
gold standard, is time consuming and often subject to human bias. On the other
hand, current state-of-the-art fully automated lung segmentation methods fail
to make their way into the clinical practice due to their inability to
efficiently incorporate human input for handling misclassifications and praxis.
This paper presents a lung annotation tool for CT images that is interactive,
efficient, and robust. The proposed annotation tool produces an "as accurate as
possible" initial annotation based on the fuzzy-connectedness image
segmentation, followed by efficient manual fixation of the initial extraction
if deemed necessary by the practitioner. To provide maximum flexibility to the
users, our annotation tool is supported in three major operating systems
(Windows, Linux, and the Mac OS X). The quantitative results comparing our free
software with commercially available lung segmentation tools show higher degree
of consistency and precision of our software with a considerable potential to
enhance the performance of routine clinical tasks.Comment: 4 pages, 6 figures; to appear in the proceedings of 36th Annual
International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBC 2014
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