Dense Volume to Volume Vascular Boundary Detection
In this work, we tackle the important problem of dense 3D volume labeling in medical imaging. We start by introducing HED 3D, a 3D extension of the state of the art 2D edge detector (HED) Next, we develop a novel 3D Convolutional Neural Network (CNN) architecture, I2I 3D, that predicts boundary location in volumetric data. Our fine to fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end to end volumetric label training and prediction (2) precise voxel level prediction to capture fine scale structures prevalent in medical data and (3) directed multi scale, multi level feature learning. We evaluate our approaches on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. We show that our deep learning approaches out perform the current state of the art in 3D vascular boundary detection (structured forests 3D) by a large margin, as well as HED applied to slices. Prediction takes about one minute on a typical (512\\times ,512\\times ,512\ volume, when using GPU.
MICCAI