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Found 6 papers, 1 papers with code
DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0. 00001)
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces
The interactive element of the framework allows the user to precisely edit the contour in real-time, and by utilising BEAS it ensures the final contour is smooth and anatomically plausible.
LNDb: A Lung Nodule Database on Computed Tomography
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients.
Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists' screening performance
Likewise, combining the findings of radiologist with the detection algorithm only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis.
