DeepAI AI Chat
Log In Sign Up

CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation

by   Wei Dai, et al.

Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation. To address these challenges, most deep learning approaches typically enhance their learning capability by substantially increasing the complexity or the number of trainable parameters within their models. Consequently, these models generally require long inference time on standard workstations operating clinical MR systems and are restricted to high-performance computing hardware due to their large memory requirement. Further, to fit 3D dataset through these large models using limited computer memory, trade-off techniques such as patch-wise training are often used which sacrifice the fine-scale geometric information from input images which could be clinically significant for diagnostic purposes. To address these challenges, we present a compact convolutional neural network with a shallow memory footprint to efficiently reduce the number of model parameters required for state-of-art performance. This is critical for practical employment as most clinical environments only have low-end hardware with limited computing power and memory. The proposed network can maintain data integrity by directly processing large full-size 3D input volumes with no patches required and significantly reduces the computational time required for both training and inference. We also propose a novel loss function with extra shape constraint to improve the accuracy for imbalanced classes in 3D MR images.


page 5

page 14

page 15

page 16


Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Accurate segmentation of the prostate from magnetic resonance (MR) image...

APNet: Semantic Segmentation for Pelvic MR Image

One of the time-consuming routine work for a radiologist is to discern a...

Holistic Decomposition Convolution for Effective Semantic Segmentation of 3D MR Images

Convolutional Neural Networks (CNNs) have achieved state-of-the-art perf...

Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network

Over the past decades, state-of-the-art medical image segmentation has h...

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art perf...

Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation

The 3D morphology and quantitative assessment of knee articular cartilag...

Probabilistic 3D surface reconstruction from sparse MRI information

Surface reconstruction from magnetic resonance (MR) imaging data is indi...