Source-linked AI summary
CFCM: Segmentation via Coarse to Fine Context Memory
Fausto Milletari, Nicola Rieke, Maximilian Baust, Marco Esposito, Nassir Navab
TL;DR
Integrating multi-scale features for full-resolution segmentation remains an active research challenge, while common skip-connection approaches mostly use summation or concatenation. CFCM uses a ResNet encoder and convolutional-LSTM decoder, and the authors report superior performance on two segmentation datasets.
Problem
Integrating learned multi-scale features to produce full-resolution segmentations remains an active research area, and alternatives to summation or concatenation are rarely investigated.
Source: §1 · pp. 1–2, §2.1 · p. 3Method
Results
The authors report that CFCM achieves superior performance and can outperform generic and application-specific networks on two segmentation databases.
Source: §4 · p. 7Takeaways & Limitations
The evaluation spans chest radiographs and video of surgical instruments during endoscopy, supporting the reported conclusion across these two settings.
Source: §4 · p. 7
Abstract
from arXiv · showhide
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.
1 Introduction and previous work
CFCM uses LSTM memory to integrate features across scales, addressing the limited investigation of alternatives to common fusion schemes. This builds on segmentation architectures that forward features through skip connections, usually using summation or concatenation.
1 Introduction and previous work
- Although CNNs learn rich multi-scale representations, integrating them to produce full-resolution segmentations remains an active research area.Multi-scale features have long been used in computer vision and segmentation.Source: §1 · p. 1
- Most current segmentation architectures use skip connections for multi-scale integration, but typically fuse features by concatenation or summation [5,14,16,19].The gated feedback refinement network is cited as an exception.Source: §1 · p. 2
- CFCM is an LSTM-based alternative for multi-scale feature integration, using memory to manage features from different resolutions.The authors compare it with established architectures on two datasets.Source: §1 · p. 2
2 Method
The method uses a ResNet encoder and convolutional-LSTM decoder to integrate features across scales. Encoder blocks provide a coarse-to-fine sequence, and the decoder maintains context as it incorporates finer details.
2 Method
- The fully convolutional architecture pairs a standard ResNet encoder with a convolutional-LSTM decoder that fuses features from different encoder layers.The LSTMs process global context from deep activations before finer details from shallower, higher-resolution features.Source: §2 · p. 2
2.1 Encoder
- ResNet block outputs form a coarse-to-fine sequence, from deep, low-resolution features with large receptive fields to shallow, high-resolution details.The figure caption notes that block and skip-connection counts vary across ResNet-18, -34, -50, and -101.Source: §2.1 · pp. 3–4, Figure 1 · p. 3
2.2 Decoder
- Each encoder block is a decoder time-step; convolutional LSTMs combine inputs with hidden states and selectively update their internal states.The schematic contrasts convolutional-LSTM fusion with summation and concatenation.Source: §2.2 · p. 4, Figure 2 · p. 4
- Decoder states are initialized from zero at the first cell and upsampled states thereafter, preserving global context while adding finer details.The final hidden state feeds the last two convolutional layers that produce the segmentation.Source: §2.2 · p. 4
3 Experimental evaluation
The experiments evaluate CFCM on chest X-rays and endoscopic surgery sequences, using three-fold cross-evaluation for lung segmentation and surgery-held-out cross-validation for instrument segmentation. CFCM is reported to outperform comparison methods, with reduced shoulder leakage on X-rays and improved robustness to specular noise and flexible grasper segmentation on EndoVis.
Montgomery County X-ray evaluation
- On Montgomery County X-rays, CFCM achieves consistently better quantitative results and reduces shoulder leakage while capturing lung shape and fine outlines.The study uses three-fold cross-evaluation on 138 annotated chest X-rays; ResNet with simple skip connections is reported to overfit as depth increases.Source: §3 · p. 5, Table 1 · p. 6
Experimental setup
- The evaluation uses 138 annotated Montgomery County X-rays and six ex-vivo EndoVis sequences, with cross-validation leaving one surgery out of training.Networks use batch size 16, a learning rate of 0.00001, and 256 × 256 input images; training lasts 150 epochs for Montgomery and 30 for EndoVis.Source: §3 · pp. 5–6
EndoVis evaluation
- CFCM outperforms state-of-the-art methods on both binary and multi-class EndoVis instrument segmentation.The comparison includes generic methods and approaches published specifically for instrument segmentation.Source: §3 · p. 6, Table 2 · p. 7
- On EndoVis, CFCM is more robust to specular noise and more precise for the grasper, recovering fine segmentation where competing methods struggle.The authors attribute this recovery to deep feature integration with LSTMs.Source: Figure 4 · p. 6, §3 · p. 7
4 Conclusion
The paper concludes that CFCM integrates multi-scale features through LSTMs and outperforms generic and application-specific networks on chest-radiograph and surgical-instrument segmentation. It identifies 3D extension and exploration of other memory mechanisms as future research directions.
4 Conclusion
- CFCM uses LSTMs for multi-scale feature integration and outperforms generic and application-specific networks on chest-radiograph and endoscopic surgical-instrument segmentation.The authors suggest extending the concept to 3D and exploring different memory mechanisms.Source: §4 · p. 7