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Adhesions are an important cause of chronic pain following abdominal surgery.Recent developments in abdominal cine-MRI have enabled the non-invasivediagnosis of adhesions. Adhesions are identified on cine-MRI by the absence ofsliding motion during movement. Diagnosis and mapping of adhesions improves themanagement of patients with pain. Detection of abdominal adhesions on cine-MRIis challenging from both a radiological and deep learning perspective. We focuson classifying presence or absence of adhesions in sagittal abdominal cine-MRIseries. We experimented with spatio-temporal deep learning architecturescentered around a ConvGRU architecture. A hybrid architecture comprising aResNet 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 to0.83 ($p<0.05$). Our full temporal classification approach adds only a smallamount (5%) of parameters to the entire architecture, which may be useful forother medical imaging problems with a temporal dimension.

Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning