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Conference Papers Year : 2020

Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images

Abstract

Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoption of the joint models are hindered: 1) the diversity of augmentation data is reduced limiting the further enhancement of segmentation, 2) misaligned regions in weakly supervised data disturb the training process, 3) lack of label-based region constraints in few-shot situation limits the registration performance. We propose a novel Deep Complementary Joint Model (DeepRS) for complex scene registration and few-shot segmentation. We embed a perturbation factor in the registration to increase the activity of deformation thus maintaining the augmentation data diversity. We take a pixel-wise discriminator to extract alignment confidence maps which highlight aligned regions in weakly supervised data so the misaligned regions’ disturbance will be suppressed via weighting. The outputs from segmentation model are utilized to implement deep-based region constraints thus relieving the label requirements and bringing fine registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge [42] show great advantages of our DeepRS that outperforms the existing state-of-the-art models.
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Dates and versions

hal-02926237 , version 1 (31-08-2020)

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Yuting He, Tiantian Li, Guanyu Yang, Youyong Kong, Yang Chen, et al.. Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images. 16th European Conference on Computer Vision (ECCV 2020), Aug 2020, Glasgow, United Kingdom. pp.770-786, ⟨10.1007/978-3-030-58523-5_45⟩. ⟨hal-02926237⟩
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