Automatic Detection of Multiple Organs Using Convolutional Neural Networks

We aim to automatically localize multiple organs in a variety of three-dimensional full body CT volumes. We propose performing feature extraction on the CT volumes from the last linear layer of the deep convolutional neural network GoogLeNet, pre-trained on the dataset from the ILSVRC 2014 classification challenge, with subsequent SVM classification. We manually annotated tight bounding boxes around the organs for each patient to use as our ground truth. This method does well when each slice from the CT volumes is divided into large patches and labelled according to their level of intersection with the ground truth. This project has real world applications in fat quantification, radiology, and organ segmentation. Keywords—convolutional neural networks; medical imaging; CT; GoogLeNet; SVM; deep learning; organ detection