{Reference Type}: Journal Article
{Title}: Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.
{Author}: Chang CY;Buckless C;Yeh KJ;Torriani M;
{Journal}: Skeletal Radiol
{Volume}: 0
{Issue}: 0
{Year}: Jul 2021 22
{Factor}: 2.128
{DOI}: 10.1007/s00256-021-03873-x
{Abstract}: <B>OBJECTIVE: </B>To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.<BR><B>METHODS: </B>Our study was IRB-approved and HIPAA-compliant. Cases of confirmed sclerotic bone metastases in chest, abdomen, and pelvis CTs were identified. Images were manually segmented for 3 classes: background, normal bone, and sclerotic lesion(s). If multiple lesions were present on a slice, all lesions were segmented. A total of 600 images were obtained, with a 90/10 training/testing split. Images were stored as 128 × 128 pixel grayscale and the training dataset underwent a processing pipeline of histogram equalization and data augmentation. We trained our model from scratch on Keras/TensorFlow using an 80/20 training/validation split and a U-Net architecture (64 batch size, 100 epochs, dropout 0.25, initial learning rate 0.0001, sigmoid activation). We also tested our model's true negative and false positive rate with 1104 non-pathologic images. Global sensitivity measured model detection of any lesion on a single image, local sensitivity and positive predictive value (PPV) measured model detection of each lesion on a given image, and local specificity measured the false positive rate in non-pathologic bone.<BR><B>RESULTS: </B>Dice scores were 0.83 for lesion, 0.96 for non-pathologic bone, and 0.99 for background. Global sensitivity was 95% (57/60), local sensitivity was 92% (89/97), local PPV was 97% (89/92), and local specificity was 87% (958/1104).<BR><B>CONCLUSIONS: </B>A deep convolutional neural network has the potential to assist in detecting sclerotic spinal metastases.