  Current artificial intelligence (AI) algorithms for short-axis cardiac
magnetic resonance (CMR) segmentation achieve human performance for slices
situated in the middle of the heart. However, an often-overlooked fact is that
segmentation of the basal and apical slices is more difficult. During manual
analysis, differences in the basal segmentations have been reported as one of
the major sources of disagreement in human interobserver variability. In this
work, we aim to investigate the performance of AI algorithms in segmenting
basal and apical slices and design strategies to improve their segmentation. We
trained all our models on a large dataset of clinical CMR studies obtained from
two NHS hospitals (n=4,228) and evaluated them against two external datasets:
ACDC (n=100) and M&Ms (n=321). Using manual segmentations as a reference, CMR
slices were assigned to one of four regions: non-cardiac, base, middle, and
apex. Using the nnU-Net framework as a baseline, we investigated two different
approaches to reduce the segmentation performance gap between cardiac regions:
(1) non-uniform batch sampling, which allows us to choose how often images from
different regions are seen during training; and (2) a cardiac-region
classification model followed by three (i.e. base, middle, and apex)
region-specific segmentation models. We show that the classification and
segmentation approach was best at reducing the performance gap across all
datasets. We also show that improvements in the classification performance can
subsequently lead to a significantly better performance in the segmentation
task.
