Abstract To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for automatic segmentation ventricular volume preterm infants with post haemorrhagic dilatation (PHVD). We trained 2D convolutional neural network (CNN) from 3D US PHVD. The method was validated Dice similarity coefficient (DSC) intra-class (ICC) compared to manual segm...

today radiology and imaging are the only and main method to generate geometrical data from body. 3d model of the body will give us the best and reliable information for better understanding and diagnostic. on the other hand with development of cad/cam and rapid prototyping, the 3d printers were born to build 3d models without considering the geometry. today the 3d printers are able to makes a 3...

Freehand 3D ultrasound is acquired by attaching a position sensor to the probe of a conventional 2D diagnostic ultrasound machine. As the clinician moves the probe, its position and orientation is recorded with respect to some fixed datum. The 2D ultrasound images are also recorded. If both the images and the position readings are independently time-stamped then they can be matched up to create...