DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (1707.00652v3)

Abstract: Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

• The paper introduces a deep interactive framework that refines segmentation accuracy by embedding user geodesic inputs into CNN architectures.
• It proposes a resolution-preserving network and an integrated back-propagatable CRF to maintain spatial consistency and detail.
• Validated on 2D placenta and 3D brain tumor cases, the method achieved significant Dice coefficient improvements with fewer user interactions.

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

The paper "DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation" presents a novel approach to enhancing the precision and robustness of medical image segmentation, a critical task in clinical applications such as diagnosis and surgical planning. The proposed method bridges the gap between fully automatic segmentation systems, which are often inadequate for precise clinical use, and interactive segmentation techniques that leverage user input to refine initial segmentation results.

The core contribution of this work is a deep learning framework that incorporates user interactions into a convolutional neural network (CNN) architecture to improve segmentation accuracy. Specifically, the system uses two CNNs: an initial segmentation proposal network (P-Net) to generate an automatic segmentation and a refinement network (R-Net) that integrates user interactions to refine this initial result. User interactions are encoded via geodesic distance transforms, enabling the system to effectively leverage anatomical knowledge provided by users. This approach improves segmentation accuracy with fewer user interventions than traditional methods, enhancing efficiency.

The paper makes several significant claims and contributions:

1. Geodesic Distance Transform: The authors employ geodesic distance transforms to encode user interactions with respect to the image context, which outperforms the use of simple Euclidean distances. This technique ensures that user interactions contribute more effectively to segmentation refinements.
2. Resolution-Preserving Network: A resolution-preserving CNN structure is proposed to maintain feature map resolution throughout the network, thus enhancing segmentation detail and accuracy compared to traditional CNN structures that suffer from accumulated downsampling effects.
3. CRF Integration: The innovative integration of a back-propagatable Conditional Random Field (CRF) into their framework allows user interactions to be embedded as hard constraints, providing spatial consistency and improving the robustness of the segmentation process.
4. Application and Validation: The framework is validated on 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images, showing significant improvements over existing automatic and interactive segmentation methods.

Numerical results demonstrate substantial gains with the proposed DeepIGeoS framework. In the case of 2D placenta segmentation, the method achieved a Dice coefficient of 89.31% after interactive refinement—a notable improvement from 85.86% achieved by automatic methods alone. For 3D brain tumor segmentation, the framework achieved a Dice coefficient of 89.93% with fewer user interactions and less time compared to conventional interactive methods like GeoS and ITK-SNAP.

Theoretical implications of this work suggest a robust methodology for effectively integrating human expertise with state-of-the-art machine learning techniques in medical imaging. It paves the way for future research into interactive deep learning frameworks that balance automated analysis with human insight, potentially extending to applications beyond medical imaging.

Practical applications of this research are promising as well, particularly in clinical settings where user-assisted segmentation is preferred for its reliability and accuracy. This framework could significantly streamline workflows in radiology departments, offering clinicians faster and more accurate tools for diagnosis and treatment planning.

Future developments could explore further optimization of user interaction encoding and integrating advanced learning paradigms like active learning to propose interactions automatically. Extending this approach to cover various imaging modalities and anatomical structures could further enhance its clinical utility. Overall, DeepIGeoS stands as a noteworthy advancement in medical image segmentation by effectively marrying deep learning with interactive refinement.