DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration

Prateek Gupta; Hanna Siebert; Mattias P. Heinrich; Kumar T. Rajamani

doi:10.1117/12.2582176

15 February 2021 DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration

Prateek Gupta, Hanna Siebert, Mattias P. Heinrich, Kumar T. Rajamani

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960Q (2021) https://doi.org/10.1117/12.2582176
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Medical image registration over the past many years has been dominated by techniques which rely on expert an- notations, while not taking advantage of the unlabelled data. Deep unsupervised architectures utilized this widely available unlabelled data to model the anatomically induced patterns in a dataset. Deformable Auto-encoder (DAE), an unsupervised group-wise registration technique, has been used to generate a deformed reconstruction of an input image, which also subsequently generates a global template to capture the deformations in a medical dataset. DAEs however have significant weakness in propagating global information over range long dependencies, which may affect the registration performance on quantitative and qualitative measures. Our proposed method captures valuable knowledge over the whole spatial dimension using an attention mechanism. We present Deformable Auto-encoder Attention Relu Network (DA-AR-Net), which is an exquisite integration of the Attention Relu(Arelu), an attention based activation function into the DAE framework. A detachment of the template image from the deformation field is achieved by encoding the spatial information into two separate latent code representation. Each latent code is followed by a separate decoder network, while only a single encoder is used for feature encoding. Our DA-AR-Net is formalized after an extensive and systematic search across various hyper-parameters - initial setting of learnable parameters of Arelu, the appropriate positioning of Arelu, latent code dimensions, and batch size. Our best architecture shows a significant improvement of 42% on MSE score over previous DAEs and 32% reduction is attained while generating visually sharp global templates.

Conference Presentation

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Prateek Gupta, Hanna Siebert, Mattias P. Heinrich, and Kumar T. Rajamani "DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960Q (15 February 2021); https://doi.org/10.1117/12.2582176

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