  Anterior chamber angle (ACA) classification is a key step in the diagnosis of
angle-closure glaucoma in Anterior Segment Optical Coherence Tomography
(AS-OCT). Existing automated analysis methods focus on a binary classification
system (i.e., open angle or angle-closure) in a 2D AS-OCT slice. However,
clinical diagnosis requires a more discriminating ACA three-class system (i.e.,
open, narrow, or synechiae angles) for the benefit of clinicians who seek
better to understand the progression of the spectrum of angle-closure glaucoma
types. To address this, we propose a novel sequence multi-scale aggregation
deep network (SMA-Net) for open-narrow-synechiae ACA classification based on an
AS-OCT sequence. In our method, a Multi-Scale Discriminative Aggregation (MSDA)
block is utilized to learn the multi-scale representations at slice level,
while a ConvLSTM is introduced to study the temporal dynamics of these
representations at sequence level. Finally, a multi-level loss function is used
to combine the slice-based and sequence-based losses. The proposed method is
evaluated across two AS-OCT datasets. The experimental results show that the
proposed method outperforms existing state-of-the-art methods in applicability,
effectiveness, and accuracy. We believe this work to be the first attempt to
classify ACAs into open, narrow, or synechia types grading using AS-OCT
sequences.
