A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks

Abstract

Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning methods. To accommodate the large variability in metastatic lesion sizes, we develop a Siamese deep neural network approach comprising three identical subnetworks for multi-resolution analysis and detection of spinal metastasis. At each location of interest, three image patches at three different resolutions are extracted and used as the input to the networks. To further reduce the false positives (FPs), we leverage the similarity between neighboring MRI slices, and adopt a weighted averaging strategy to aggregate the results obtained by the Siamese neural networks. The detection performance is evaluated on a set of 26 cases using a free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach correctly detects all the spinal metastatic lesions while producing only 0.40 FPs per case. At a true positive (TP) rate of 90%, the use of the aggregation reduces the FPs from 0.375 FPs per case to 0.207 FPs per case, a nearly 44.8% reduction. The results indicate that the proposed Siamese neural network method, combined with the aggregation strategy, provide a viable strategy for the automated detection of spinal metastasis in MRI images.

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

“…(49,50) Automated detection of the fascia lata in the thigh was used to assess for fatty replacement of muscle in patients with muscular dystrophy. (49,50) Automated detection of the fascia lata in the thigh was used to assess for fatty replacement of muscle in patients with muscular dystrophy.…”

“…Neoplasia detection in bone on MRI has been performed by Jerebko and Wang. (49,50) Automated detection of the fascia lata in the thigh was used to assess for fatty replacement of muscle in patients with muscular dystrophy. (51) Knee cartilage defects and meniscal tears have been automatically assessed on MRI.…”

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body

“…This is in line with previous studies where human experts missed 1 in 4 breast cancer metastases in histopathology 27 , an issue that even increases substantially if humans work under time pressure 28 . Motivated by this, deep learning based approaches for cancer and metastasis detection recently gained substantial momentum for various imaging modalities, also beyond microscopy [29][30][31][32] .…”

Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

“…It can automatically determine the categories of samples by calculating similarity, which is very suitable for classification using a few samples from the training set. At present, effective applications based on the Siamese network focus on data classification such as signatures recognition [19,20], disease diagnosis [21] and object tracking [22]. To the best of our knowledge, there is no report on the use of the Siamese network to classify power quality disturbances.…”