Title: ConvNet-Based Localization of Anatomical Structures in 3D Medical Images,
Abstract: Localization of anatomical structures is a prerequisite for many tasks in
medical image analysis. We propose a method for automatic localization of one
or more anatomical structures in 3D medical images through detection of their
presence in 2D image slices using a convolutional neural network (ConvNet).
A single ConvNet is trained to detect presence of the anatomical structure of
interest in axial, coronal, and sagittal slices extracted from a 3D image. To
allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling
is applied. After detection, 3D bounding boxes are created by combining the
output of the ConvNet in all slices.
In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100
abdomen CT scans were used. The heart, ascending aorta, aortic arch, and
descending aorta were localized in chest CT scans, the left cardiac ventricle
in cardiac CTA scans, and the liver in abdomen CT scans. Localization was
evaluated using the distances between automatically and manually defined
reference bounding box centroids and walls.
The best results were achieved in localization of structures with clearly
defined boundaries (e.g. aortic arch) and the worst when the structure boundary
was not clearly visible (e.g. liver). The method was more robust and accurate
in localization multiple structures.