To identify congenital anomalies, pregnant women are offered a structural screening using ultrasound during the first trimester. This screening is performed in two-dimensional ultrasound and has a duration of up to 45 minutes. While three-dimensional (3D) and 3D virtual reality (VR) ultrasound have shown to be beneficial for the anomaly detection rate of some fetal structures, their implementation is limited by an increased evaluation time and the need for expert knowledge. To address these problems and reduce operator dependency, an automatic organ detection model for first-trimester fetal ultrasound is proposed. We used 69 3D ultrasound scans from the VR-FETUS study with a gestational age ranging from 11+0 weeks and 13+6 weeks. These scans were annotated in VR with sparse labels (landmarks) for the heart, lungs, spine, choroid plexuses, cerebellum, mandible, orbits, upper lip and nasal bone, while dense labels (segmentations) were provided for the bladder, kidneys and stomach. In total 50 scans were used for training and 19 scans were used for testing. We trained nnU-Net models for each organ separately, using pseudo segmentation labels that were created from the landmarks. Furthermore, it was tested if using a combination of labels, additional training data or different loss functions would increase model performance. The Dice similarity coefficient (DSC) was used for evaluation of the segmentation labels and we assessed the detection rate for all models. The heart, lungs and mandible were detected in 95 to 100% of the test scans, while the cerebellum and plexuses were detected in 63 to 79% of the test scans. The detection rate for the orbits and upper lip was lower than 43% and the nasal bone was not detected by any model. The median DSC for the bladder, kidneys and stomach ranged between 0.70 and 0.81 and they were detected in 89 to 100% of the test scans. With our results we take the first step towards automatic organ detection in first-trimester 3D ultrasound, by lowering the evaluation time and reducing the operator dependency.