Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review.

The pancreas is a highly variable organ, the size, shape, and position of which are affected by age, sex, adiposity, the presence of diseases affecting the pancreas (e.g., diabetes, pancreatic cancer, pancreatitis) and other factors. Accurate automated segmentation of the pancreas has the potential to facilitate timely diagnosing and managing of diseases of the endocrine and exocrine pancreas. The aim was to systematically review studies reporting on automated pancreas segmentation algorithms derived from computed tomography (CT) or magnetic resonance (MR) images. The MEDLINE database and three patent databases were searched. Data on the performance of algorithms were meta-analysed, when possible. The algorithms were classified into one of four groups: multiorgan atlas-based, landmark-based, shape model-based, and neural network-based. A total of 13 cohorts suitable for meta-analysis were pooled to determine the performance of pancreas segmentation algorithms altogether using the Dice coefficient. These cohorts, comprising 1110 individuals, yielded a weighted mean Dice coefficient of 74.4%. Eight cohorts suitable for meta-analysis were pooled to determine the performance of pancreas segmentation algorithms altogether using the Jaccard index. These cohorts, comprising 636 individuals, yielded a weighted mean Jaccard index of 63.7%. Multiorgan atlas-based algorithms had a weighted mean Dice coefficient of 70.1% and a weighted mean Jaccard index of 59.8%. Neural network-based algorithms had a weighted mean Dice coefficient of 82.3% and a weighted mean Jaccard index of 70.1%. Studies using the other two types of algorithms were not meta-analysable. The above findings indicate that the automation of pancreas segmentation represents a considerable challenge as the performance of current automated pancreas segmentation algorithms is suboptimal. Adopting standardised reporting on performance of pancreas segmentation algorithms and encouraging the use of benchmark pancreas segmentation datasets will allow future algorithms to be tested and compared more easily and fairly.