  May-Thurner Syndrome (MTS), also known as iliac vein compression syndrome or
Cockett's syndrome, is a condition potentially impacting over 20 percent of the
population, leading to an increased risk of iliofemoral deep venous thrombosis.
In this paper, we present a 3D-based deep learning approach called MTS-Net for
diagnosing May-Thurner Syndrome using CT scans. To effectively capture the
spatial-temporal relationship among CT scans and emulate the clinical process
of diagnosing MTS, we propose a novel attention module called the dual-enhanced
positional multi-head self-attention (DEP-MHSA). The proposed DEP-MHSA
reconsiders the role of positional embedding and incorporates a dual-enhanced
positional embedding in both attention weights and residual connections.
Further, we establish a new dataset, termed MTS-CT, consisting of 747 subjects.
Experimental results demonstrate that our proposed approach achieves
state-of-the-art MTS diagnosis results, and our self-attention design
facilitates the spatial-temporal modeling. We believe that our DEP-MHSA is more
suitable to handle CT image sequence modeling and the proposed dataset enables
future research on MTS diagnosis. We make our code and dataset publicly
available at: https://github.com/Nutingnon/MTS_dep_mhsa.
