3D Pyramid Pooling Network for Abdominal MRI Series Classification

Zhu, Zhe; Mittendorf, Amber; Shropshire, Erin; Allen, Brian; Miller, Chad; Bashir, Mustafa R.; Mazurowski, Maciej A.

doi:10.1109/tpami.2020.3033990

Cited by 4 publications

(1 citation statement)

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“…PSPNet [29] adopts a pyramid pooling module (PPM) containing parallel sub-region average pooling layers followed by upsampling to fuse the multi-scale sub-region representations. This module has shown significant improvement and is frequently used in semantic segmentation architectures [30]. Atrous spatial pyramid pooling (ASPP) [31], [32] was proposed to deal with objects' scale variance as an efficient alternative to Share-net [33].…”

Section: Related Workmentioning

confidence: 99%

DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos

Ghamsarian,

Taschwer,

Schoeffmann

2021

Preprint

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Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant instances make the designation of a unique network quite challenging. This paper proposes a semantic segmentation network termed as DeepPyram that can achieve superior performance in segmenting relevant objects in cataract surgery videos with varying issues. This superiority mainly originates from three modules: (i) Pyramid View Fusion, which provides a varying-angle global view of the surrounding region centering at each pixel position in the input convolutional feature map; (ii) Deformable Pyramid Reception, which enables a wide deformable receptive field that can adapt to geometric transformations in the object of interest; and (iii) Pyramid Loss that adaptively supervises multiscale semantic feature maps. These modules can effectively boost semantic segmentation performance, especially in the case of transparency, deformability, scalability, and blunt edges in objects. The proposed approach is evaluated using four datasets of cataract surgery for objects with different contextual features and compared with thirteen state-of-the-art segmentation networks. The experimental results confirm that DeepPyram outperforms the rival approaches without imposing additional trainable parameters. Our comprehensive ablation study further proves the effectiveness of the proposed modules.

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