Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Convolutional Neural Networks are being increasingly applied to the detection of anomalies in Computed Tomographies (CTs). The goal of this paper is to implement an automated Computer-Aided Detection (CADe) system for spinal lesions using CTs and Convolutional Neural Networks pre-trained on commercial datasets. The proposed pipeline works as follows. The CADe takes in input CT scans and is equipped with an intuitive GUI to allow physicians to use it as a support tool for diagnoses. From the CT scans, the CADe selects volumes containing the vertebrae and extracts 2D slices of these volumes. These slices are pre-processed and then analyzed using a VGG19 Convolutional Neural Network and a tailored, binary classifier. The neural network identifies healthy vertebrae and vertebrae containing lesions (e.g. metastases, primary tumors, lytic and sclerotic lesions). For training and testing purposes, we generated a dataset from CTs of vertebrae from patients treated at IRCCS Rizzoli Orthopaedics Institute of Bologna, Italy, between 2009 and 2019. Both healthy and lesioned vertebrae retrieved with different tomography machines and setups are considered. The dataset has been enlarged using data augmentation techniques and subsequently used to train a wide range of deep learning models. We perform an in-depth benchmark study to assess the performance of the considered classifier against different Convolutional Neural Networks pre-trained on the ImageNet dataset and exploiting Transfer Learning techniques. Leveraging Transfer Learning techniques, we reached 93.43% accuracy and a recall of 92.99%.