  Blastomere instance segmentation is important for analyzing embryos'
abnormality. To measure the accurate shapes and sizes of blastomeres, their
amodal segmentation is necessary. Amodal instance segmentation aims to recover
the complete silhouette of an object even when the object is not fully visible.
For each detected object, previous methods directly regress the target mask
from input features. However, images of an object under different amounts of
occlusion should have the same amodal mask output, which makes it harder to
train the regression model. To alleviate the problem, we propose to classify
input features into intermediate shape codes and recover complete object shapes
from them. First, we pre-train the Vector Quantized Variational Autoencoder
(VQ-VAE) model to learn these discrete shape codes from ground truth amodal
masks. Then, we incorporate the VQ-VAE model into the amodal instance
segmentation pipeline with an additional refinement module. We also detect an
occlusion map to integrate occlusion information with a backbone feature. As
such, our network faithfully detects bounding boxes of amodal objects. On an
internal embryo cell image benchmark, the proposed method outperforms previous
state-of-the-art methods. To show generalizability, we show segmentation
results on the public KINS natural image benchmark. To examine the learned
shape codes and model design choices, we perform ablation studies on a
synthetic dataset of simple overlaid shapes. Our method would enable accurate
measurement of blastomeres in in vitro fertilization (IVF) clinics, which
potentially can increase IVF success rate.
