An application of cascaded 3D fully convolutional networks for medical image segmentation (1803.05431v2)

Abstract: Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. Our code and trained models are available for download: https://github.com/holgerroth/3Dunet_abdomen_cascade.

Application of Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation

This paper presents an innovative approach to the segmentation of medical images by employing cascaded 3D fully convolutional networks (FCNs), specifically 3D U-Nets, for the segmentation of computed tomography (CT) scans. It effectively addresses the challenge of segmenting complex multilayered anatomical structures by introducing a two-stage, coarse-to-fine segmentation strategy.

Key Contributions and Methodology

The novel approach proposed by the authors involves the use of a two-stage strategy where the first stage uses a 3D FCN to coarsely delineate the candidate regions in a volumetric image, significantly reducing the number of voxels targeted for subsequent analysis. In the second stage, a second 3D FCN focuses on finely segmenting organs and vessels within these candidate regions. This method allows the network to concentrate on more detailed and accurate segmentation without the overwhelming information presented by the entire image, enhancing efficiency while maintaining accuracy.

One of the significant achievements of this methodology is its ability to deliver high accuracy without the need for extensive handcrafting of features or class-specific models. The cascaded approach efficiently handles class imbalance by initially detecting larger regions of interest and then focusing on more detailed boundaries in the subsequent stages. The paper provides strong evidence of this approach's efficacy through experiments conducted on datasets of abdominal CT images, demonstrating significant improvement in the Dice similarity score for the segmentation of challenging organs such as the pancreas, where scores improved from 68.5% to 82.2%.

Practical Implications

This research holds notable implications for medical imaging, particularly in the domain of automated anatomical segmentation. It suggests robustness not only in accuracy and efficiency but also in its adaptability to different datasets and medical imaging environments. The ability of the cascaded approach to concentrate computational resources more effectively makes it suitable for real-time applications in clinical settings, potentially enhancing pre-surgical planning and diagnostic accuracy.

Theoretical Implications and Future Directions

The success of this cascaded FCN approach opens new avenues for further exploration in the domain of 3D medical imaging. This paper suggests that the fusion of 2D and 3D convolutional kernels might extract a richer set of features, potentially improving segmentation performance further. The approach’s flexibility suggests it could be adapted for use in other imaging modalities and more complex anatomical structures.

Looking forward, the combination of these networks with anatomical constraints and multi-modal image data could further enhance accuracy and reliability. Additionally, as GPU capabilities continue to advance, this will allow larger field-of-view inputs without as much need for computational trimming or subvolume processing, thereby simplifying the training and inference phases.

Conclusion

The cascaded deployment of 3D FCNs as detailed in this paper represents a meaningful advancement in medical image segmentation. By improving the accuracy of intricate organ segmentation in CT images, this research provides a foundation for more effective and efficient machine learning-assisted medical imaging. Given these strong results, the methods outlined warrant further exploration and adaptation, potentially influencing future developments in both the technological aspects of FCNs and their clinical applications.