# ConvNet-Based Localization of Anatomical Structures in 3D Medical Images

## TL;DR

This paper introduces a ConvNet-based method for automatic localization of anatomical structures in 3D medical images by detecting their presence in 2D slices and constructing 3D bounding boxes, demonstrating high accuracy especially for well-defined structures.

## Contribution

The paper presents a novel approach combining ConvNet detection with spatial pyramid pooling for 3D structure localization in medical images, improving robustness and accuracy.

## Key findings

- High accuracy in localizing structures with clear boundaries
- Robust detection of multiple structures simultaneously
- Better performance in structures with distinct boundaries

## 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.