Titlebook: Bildverarbeitung für die Medizin 2019; Algorithmen – System Heinz Handels,Thomas M. Deserno,Thomas Tolxdorff Conference proceedings 2019 Sp

壯觀的游行

MUMP

Merited

GRE

Deep Segmentation Refinement with Result-Dependent Learning,ent loss adaptation. The performance of our method regarding segmentation quality is evaluated on the example of hip joint segmentation in T1-weighted MRI data sets. The results are compared to an ordinary U-Net implementation. While the segmentation quality of the proximal femur does not significan

intelligible

Abstract: Automatic Estimation of Cochlear Duct Length and Volume Size,nd volume size estimation method from clinical multi-modal medical images. The method utilizes atlas-model-based segmentation to estimate a transformation from a model to an input volume. The result is used to transform a well-defined segmentation and a points-set of a scala tympani to the input ima

overhaul

Interactive Neural Network Robot User Investigation for Medical Image Segmentation,Especially in the field of inter-operative medical image processing of a single patient, where a high accuracy is an uncompromisable necessity, a human operator guiding a system towards an optimal segmentation result is a time-efficient constellation benefiting the patient. There are recent categori

災(zāi)禍

Tracing of Nerve Fibers Through Brain Regions of Fiber Crossings in Reconstructed 3D-PLI Volumes,er structures can be accurately visualized with 3D-PLI, the currently used physical model (based on Jones Calculus) is not well suited to distinguish steep fibers from specific fiber crossings. Hence, streamline tractography algorithms tracing fiber pathways get easily misdirected in such brain regi

Mutter

爭吵加

Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT,ing emergency imaging could draw the attention of a radiologist directly to the thrombotic clot. Considering an appropriate early treatment, the immediate automatic detection of a clot could lead to a better patient outcome by reducing time-to-treatment. We present a two-stage neural network to auto

Nebulous