Automatic instance segmentation of individual vertebrae from 3D CT is essential for various applications in orthopedics, neurology, and oncology. In case model-based segmentation (MBS) shall be used to generate a mesh-based representation of the spine, a good initialization of MBS is crucial to avoid wrong vertebra labels due to the similar appearance of adjacent vertebrae. Here, we propose to use deep learning (DL) for MBS initialization and for robustly guiding MBS during segmentation to generate 24 instance segmentations for each and every vertebra. We propose a four-step approach: In step 1, we apply a first single-class U-Net to coarsely segment the spine. In step 2, we sample image patches along the coarse segmentation of step 1 and apply a second multi-class U-net to generate a fine segmentation including individual labeling of some key vertebrae and vertebra body landmarks. In step 3, we detect and label landmark coordinates from the classes estimated in step 2. In step 4, we initialize all MBS vertebrae models using the landmarks from step 3 and adapt the model to the joint vertebrae probability map from step 2. We validated our method on segmentation results from 147 patient images. We computed surface distances between segmentation and ground truth meshes and achieved root mean squared distances of RMSDist = 0.90 mm over all cases and vertebrae.
Ultrasound (US) is the modality of choice for fetal screening, which includes the assessment of a variety of standardized growth measurements, like the abdominal circumference (AC). Screening guidelines define criteria on the scan plane, in which the measurement is taken. As US is increasingly becoming a 3D modality, approaches for automated determination of the optimal scan plane in a volumetric dataset would greatly improve the workflow. In this work, a novel framework for deep hyperplane learning is proposed and applied for view plane estimation in fetal US examinations. The approach is tightly integrated in the clinical workflow and consists of two main steps. First, the bounding box around the structure of interest is determined in the central slice (MPR). Second, offsets from the structure in the bounding box to the optimal view plane are estimated. By linear regression through the estimated offsets, the view plane coordinates can then be determined. The presented approach is successfully applied on clinical screening data for AC plane estimation and a high accuracy is obtained, outperforming or comparable to recent publications on the same application.