Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

TL;DR

This paper introduces an unsupervised CNN-based method called UBR for automatically recognizing body parts in CT slices by learning a continuous body coordinate score without labeled data, leveraging the inherent slice order.

Contribution

The paper presents a novel unsupervised learning approach that exploits the spatial order of CT slices to estimate body part positions without requiring labeled datasets.

Findings

UBR accurately predicts body part positions in CT slices.

The method is fast, simple, and does not need labeled data.

Applications include network initialization and anomaly detection.

Abstract

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is not fully exploited. In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem. A novel unsupervised learning method and two inter-sample CNN loss functions are presented. Distinct from previous work, UBR builds a coordinate system for the human body and outputs a continuous score for each axial slice, representing the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled CT volumes that are abundant, thus no extra annotation effort is needed. UBR is simple, fast, and accurate. Quantitative and qualitative experiments validate its effectiveness. In addition, we show two applications of UBR in network initialization and anomaly detection.