  We propose a new Patch-based Iterative Network (PIN) for fast and accurate
landmark localisation in 3D medical volumes. PIN utilises a Convolutional
Neural Network (CNN) to learn the spatial relationship between an image patch
and anatomical landmark positions. During inference, patches are repeatedly
passed to the CNN until the estimated landmark position converges to the true
landmark location. PIN is computationally efficient since the inference stage
only selectively samples a small number of patches in an iterative fashion
rather than a dense sampling at every location in the volume. Our approach
adopts a multi-task learning framework that combines regression and
classification to improve localisation accuracy. We extend PIN to localise
multiple landmarks by using principal component analysis, which models the
global anatomical relationships between landmarks. We have evaluated PIN using
72 3D ultrasound images from fetal screening examinations. PIN achieves
quantitatively an average landmark localisation error of 5.59mm and a runtime
of 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2D
standard scan planes derived from the predicted landmark locations are visually
similar to the clinical ground truth. Source code is publicly available at
https://github.com/yuanwei1989/landmark-detection.
