Ultrasound imaging of the spine to diagnose the severity of scoliosis is a recent development in the field, offering 3D information that does not require a complicated procedure of reconstruction, unlike with radiography. Determining the severity of scoliosis on ultrasound volumes requires labelling vertebral features called laminae. To increase accuracy and reduce time spent on this task, this paper reported a novel custom centroid-based distance loss function for lamina segmentation in 3D ultrasound volumes, using convolutional neural networks (CNN). A comparison between the custom and two standard loss functions was performed by fitting a CNN with each loss function. The results showed that the custom loss network performed the best in terms of minimization of the distances between the centroids in the ground truth and the centroids in the predicted segmentation. On average, the custom network improved on the total distance between predicted and true centroids by 33 voxels (22%) when compared with the second best performing network, which used the Dice loss. In general, this novel custom loss function allowed the network to detect two more laminae on average in the lumbar region of the spine that the other networks tended to miss.