Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2016 | MICCAI | Conference |
Volume | Citations | PageRank |
abs/1605.08401 | 19 | 0.97 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jameson Merkow | 1 | 20 | 1.32 |
| David Kriegman | 2 | 7693 | 451.96 |
| Alison Marsden | 3 | 52 | 8.83 |
| Zhuowen Tu | 4 | 3663 | 215.79 |