Segmenting Cardiac Muscle Z-disks with Deep Neural Networks
Mihaela Croitor Ibrahim Nishant Ravikumar Alistair Curd Joanna Leng Oliver Umney Michelle Peckham
Z-disks are complex structures that delineate repeating sarcomeres in
striated muscle. They play significant roles in cardiomyocytes such as
providing mechanical stability for the contracting sarcomere, cell signalling
and autophagy. Changes in Z-disk architecture have been associated with
impaired cardiac function. Hence, there is a strong need to create tools to
segment Z-disks from microscopy images, that overcome traditional limitations
such as variability in image brightness and staining technique. In this study,
we apply deep learning based segmentation models to extract Z-disks in images
of striated muscle tissue. We leverage a novel Airyscan confocal dataset, which
comprises high resolution images of Z-disks of healthy heart tissue, stained
with Affimers for specific Z-disk proteins. We employed an interactive
labelling tool, Ilastik to obtain ground truth segmentation masks and use the
resulting data set to train and evaluate the performance of several
state-of-the-art segmentation networks. On the test set, UNet++ achieves best
segmentation performance for Z-disks in cardiomyocytes, with an average Dice
score of 0.91 and outperforms other established segmentation methods including
UNet, FPN, DeepLabv3+ and pix2pix. However, pix2pix demonstrates improved
generalisation, when tested on an additional dataset of cardiomyocytes with a
titin mutation. This is the first study to demonstrate that automated machine
learning-based segmentation approaches may be used effectively to segment
Z-disks in confocal microscopy images. Automated segmentation approaches and
predicted segmentation masks could be used to derive morphological features of
Z-disks (e.g. width and orientation), and subsequently, to quantify
disease-related changes to cardiac microstructure.