Sullivan, EO;
van de Lande, L;
Osolos, A;
Schievano, S;
Dunaway, DJ;
Bulstrode, N;
Zafeiriou, S;
(2020)
Ear Cartilage Inference for Reconstructive Surgery with Convolutional Mesh Autoencoders.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020.
(pp. pp. 76-85).
Springer: Cham, Switzerland.
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Abstract
Many children born with ear microtia undergo reconstructive surgery for both aesthetic and functional purposes. This surgery is a delicate procedure that requires the surgeon to carve a “scaffold” for a new ear, typically from the patient’s own rib cartilage. This is an unnecessarily invasive procedure, and reconstruction relies on the skill of the surgeon to accurately construct a scaffold that best suits the patient based on limited data. Work in stem-cell technologies and bioprinting present an opportunity to change this procedure by providing the opportunity to “bioprint” a personalised cartilage scaffold in a lab. To do so, however, a 3D model of the desired cartilage shape is first required. In this paper we optimise the standard convolutional mesh autoencoder framework such that, given only the soft tissue surface of an unaffected ear, it can accurately predict the shape of the underlying cartilage. To prevent predicted cartilage meshes from intersecting with, and protruding through, the soft tissue ear mesh, we develop a novel intersection-based loss function. These combined efforts present a means of designing personalised ear cartilage scaffold for use in reconstructive ear surgery.
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