2,017 research outputs found
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Semantic segmentation is essentially important to biomedical image analysis.
Many recent works mainly focus on integrating the Fully Convolutional Network
(FCN) architecture with sophisticated convolution implementation and deep
supervision. In this paper, we propose to decompose the single segmentation
task into three subsequent sub-tasks, including (1) pixel-wise image
segmentation, (2) prediction of the class labels of the objects within the
image, and (3) classification of the scene the image belonging to. While these
three sub-tasks are trained to optimize their individual loss functions of
different perceptual levels, we propose to let them interact by the task-task
context ensemble. Moreover, we propose a novel sync-regularization to penalize
the deviation between the outputs of the pixel-wise segmentation and the class
prediction tasks. These effective regularizations help FCN utilize context
information comprehensively and attain accurate semantic segmentation, even
though the number of the images for training may be limited in many biomedical
applications. We have successfully applied our framework to three diverse 2D/3D
medical image datasets, including Robotic Scene Segmentation Challenge 18
(ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus
Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all
three challenges.Comment: IEEE Transactions on Medical Imagin
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
While deep convolutional neural networks (CNN) have been successfully applied
for 2D image analysis, it is still challenging to apply them to 3D anisotropic
volumes, especially when the within-slice resolution is much higher than the
between-slice resolution and when the amount of 3D volumes is relatively small.
On one hand, direct learning of CNN with 3D convolution kernels suffers from
the lack of data and likely ends up with poor generalization; insufficient GPU
memory limits the model size or representational power. On the other hand,
applying 2D CNN with generalizable features to 2D slices ignores between-slice
information. Coupling 2D network with LSTM to further handle the between-slice
information is not optimal due to the difficulty in LSTM learning. To overcome
the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that
transfers convolutional features learned from 2D images to 3D anisotropic
volumes. Such a transfer inherits the desired strong generalization capability
for within-slice information while naturally exploiting between-slice
information for more effective modelling. The focal loss is further utilized
for more effective end-to-end learning. We experiment with the proposed 3D
AH-Net on two different medical image analysis tasks, namely lesion detection
from a Digital Breast Tomosynthesis volume, and liver and liver tumor
segmentation from a Computed Tomography volume and obtain the state-of-the-art
results
OctNetFusion: Learning Depth Fusion from Data
In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.Comment: 3DV 2017, https://github.com/griegler/octnetfusio
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