Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

TL;DR

This paper introduces an iterative fully convolutional neural network approach for automatic vertebra segmentation and identification that works across various imaging modalities and spine coverage, independent of vertebra visibility.

Contribution

It presents a novel iterative instance segmentation method combining CNNs with memory, enabling vertebra detection regardless of the number of visible vertebrae or scan coverage.

Findings

Outperforms state-of-the-art methods in diverse datasets

Works effectively across CT and MR modalities

Handles partial spine scans and low-dose images

Abstract

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebrae. This instance-by-instance segmentation is enabled by combining the network with a memory component that retains information about already segmented vertebrae. The network iteratively analyzes image patches, using information from both image and memory to search for the next vertebra. To efficiently traverse the image, we include the prior knowledge that the vertebrae are always located next to each other, which is used to follow the vertebral column. This method was evaluated with five diverse datasets, including multiple modalities (CT and MR), various fields of view and coverages of different sections of the spine, and a particularly challenging set of low-dose chest CT scans. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible and generalizable.