Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning
This work addresses the challenge of non-invasive diagnosis for patients with chronic pain after abdominal surgery, representing an incremental improvement in medical imaging.
The paper tackled the problem of detecting abdominal adhesions in cine-MRI using spatio-temporal deep learning, achieving an increase in classification performance from AUROC 0.74 to 0.83 with a hybrid ResNet-ConvGRU architecture.
Adhesions are an important cause of chronic pain following abdominal surgery. Recent developments in abdominal cine-MRI have enabled the non-invasive diagnosis of adhesions. Adhesions are identified on cine-MRI by the absence of sliding motion during movement. Diagnosis and mapping of adhesions improves the management of patients with pain. Detection of abdominal adhesions on cine-MRI is challenging from both a radiological and deep learning perspective. We focus on classifying presence or absence of adhesions in sagittal abdominal cine-MRI series. We experimented with spatio-temporal deep learning architectures centered around a ConvGRU architecture. A hybrid architecture comprising a ResNet followed by a ConvGRU model allows to classify a whole time-series. Compared to a stand-alone ResNet with a two time-point (inspiration/expiration) input, we show an increase in classification performance (AUROC) from 0.74 to 0.83 ($p<0.05$). Our full temporal classification approach adds only a small amount (5%) of parameters to the entire architecture, which may be useful for other medical imaging problems with a temporal dimension.