This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).
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Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic
scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic
parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight
is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to
sequentially infer intermediate concepts associated with the final task. To acquire training data in desired
quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate
large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the
network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks
including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint
localization and instance segmentation. The empirical results substantiate the utility of our deep supervision
scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less
overfitting compared to standard end-to-end training.
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- Award ID(s):
- 1637949
- NSF-PAR ID:
- 10047463
- Date Published:
- Journal Name:
- CVPR
- Page Range / eLocation ID:
- 388 to 397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CVPR.2017.49
