A Content-Driven Architecture for Medical Image Segmentation

Sabrowsky-Hirsch, Bertram; Thumfart, Stefan; Hofer, Richard R.; Fenz, Wolfgang

doi:10.1145/3442555.3442570

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2022

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“…Patch sampling [11] is also an effective data augmentation method. For example, Bertram et al [12] used a content-sensitive sampling strategy for patchwise training. The emergence of Generative Adversarial Nets (GAN) provides a new idea for image augmentation.…”

Section: Introductionmentioning

confidence: 99%

A combined deformable model and medical transformer algorithm for medical image segmentation

Tang

Duan²,

Sun³

et al. 2022

Med Biol Eng Comput

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Deep learning–based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture. Finally, a medical transformer network was constructed to segment three types of medical images, including prostate MR image, heart US image, and tongue color images. The segmentation accuracy of the three tasks achieved 89.97%, 91.90%, and 94.25%, respectively. The experimental results show that the proposed method improves medical image segmentation performance. Graphical abstract

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Section: Introductionmentioning

confidence: 99%