3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
This work addresses the challenge of pancreas segmentation in medical imaging, which is difficult due to high inter-patient variability, offering an incremental improvement over previous methods.
The paper tackles automated pancreas segmentation from CT volumes by combining 3D FCN features with regression forests for localization and a patient-specific atlas for segmentation, achieving a Jaccard index of 60.6% and Dice overlap of 73.9% on 146 CT volumes.
This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.