CFCM: Segmentation via Coarse to Fine Context Memory

By Fausto Milletarì, Nicola Rieke, Maximilian Baust, Marco Esposito, Nassir Navab. 2018

50 citations recorded by the source corpus.

Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods for feature fusion at different resolutions have been scarcely investigated for medical applications. In this work we propose to implement segmentation via an encoder-decoder architecture which differs from any other previously published method since (i) it employs a very deep architecture based on residual learning and (ii) combines features via a convolutional Long Short Term Memory (LSTM), instead of concatenation or summation. The intuition is that the memory mechanism implemented by LSTMs can better integrate features from different scales through a coarse-to-fine strategy; hence the name Coarse-to-Fine Context Memory (CFCM). We demonstrate the remarkable advantages of this approach on two datasets: the Montgomery county lung segmentation dataset, and the EndoVis 2015 challenge dataset for surgical instrument segmentation.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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In Computer Vision and Pattern Recognition

1. Tong Lu
2. Wenhai Wang
3. Yirui Wu
4. Guangchen Shi
5. J. Liu
6. Shaohua Wan
7. Luc Van Gool
8. Yu Qiao

Citations in the corpus

• Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation (2022, methodology): “Considering dependence of context information, RNN-based methods [16, 34, 44] use recurrent layers to capture local and global spatial structure information of images.”
• 2017 Robotic Instrument Segmentation Challenge (2019, unknown): “However, despite heavy usage of this dataset in subsequent publications [7]–[9], [11], [12] there are significant limitations due to the limited background variability and also misalignments due to the ground truth being generated by forward kinematics of the da Vinci Research Kit (dVRK) [13] which ”