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Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks

The document proposes a relevance-based compression method for cataract surgery videos using convolutional neural networks. The method uses Mask R-CNN to detect relevant regions like the cornea and instruments. Pixels outside these regions are removed or compressed at lower quality. Testing showed the method achieved up to 68% reduction in video size while maintaining good quality for relevant regions. The summaries provide the key information about the proposed method and results at a high level in 3 sentences or less as requested.

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Convolutional Neural Networks•
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Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks Negin Ghamsarian, Hadi Amirpour, Christian Timmerer, Mario Taschwer, Klaus Schöffmann
1. Introduction 4. Compression Results 2. Proposed Method 3. Detection Results
1. Introduction:
1. Introduction:
1. Introduction 4. Compression Results 2. Proposed Method 3. Detection Results
3. Proposed Method:
2. Proposed Method: 1 • 𝐴𝑐𝑡𝑖𝑜𝑛 2 • 𝐴𝑐𝑡𝑖𝑜𝑛 ∩ (𝑐𝑜𝑟𝑛𝑒𝑎 ∪ 𝑖𝑛𝑠𝑡𝑟𝑢𝑚𝑒𝑛𝑡) 3 • 𝐴𝑐𝑡𝑖𝑜𝑛 ∩ 𝑐𝑜𝑟𝑛𝑒𝑎 (simple) 4 • 𝐴𝑐𝑡𝑖𝑜𝑛 ∩ 𝑐𝑜𝑟𝑛𝑒𝑎 (Luma preference) 5 • 𝐴𝑐𝑡𝑖𝑜𝑛 ∩ 𝑐𝑜𝑟𝑛𝑒𝑎 (removed background) Scenarios for relevant content … … … Action Idle Action Idle Irrelevant Irrelevant Relevant
2. Proposed Method:
2. Proposed Method:
2. Proposed Method: Mask R-CNN (Mask Regional Convolutional Neural Network) Two sub-problems: Object Detection Semantic Segmentation Using region proposal networks Using fully convolutional networks
2. Proposed Method: 9 proposals relative to 9 anchors, and 4 parameters per anchor Features from the backbone network 9 × 4 = 36 Relative box parameters Class probabilities 9 × 2 = 18 RPN (Region Proposal Network):
2. Proposed Method: RPN (Region Proposal Network): 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 2 3 4 5 6 7 8 9 Class Probabilities Cornea Background 1 2 3 4 75 6 8 9
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1 2 3 4 5 6 7 8 9 Class Probabilities Cornea Background 3. Proposed Method: RPN (Region Proposal Network): 1 2 3 4 75 6 8 9
2. Proposed Method: Forward/Inference Backward/Learning FCN (Fully Convolutional Network):
1. Introduction 4. Compression Results 2. Proposed Method 3. Detection Results
3. Detection Results:
3. Detection Results: V2 V5 V7 V8 Idle Action
3. Detection Results:
3. Detection Results: At least 80% IoU for mask segmentation
3. Detection Results: At least 85% IoU for bounding-box segmentation
1. Introduction 4. Compression Results 2. Proposed Method 3. Detection Results
5. Compression Results: ∆𝑄 = 5 ∆𝑄 = 10 ∆𝑄 = 13 ∆𝑄 = 15 PSNR values for an exemplary segment using different QP differences:
5. Compression Results: The percentage of bitrate reduction resulting from different scenarios and different QP differences:
5. Compression Results: The PSNR of ROI and output size corresponding to an exemplary video compressed in different scenarios:
5. Compression Results: Original Scenario II
5. Compression Results: Original Scenario IV
5. Compression Results: Original Scenario V
Conclusion: 4 Being generalizable to all sorts of surgical videos where the domain knowledge is required 3 A set of novel scenarios to compress cataract surgery videos so as to be proportionate to the target audience.2 1 Main Contributions Relevance-based compression approach using surgical domain knowledge Storage space gain of up to 68%
Question? negin@itec.aau.at hadi@itec.aau.at
Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Neural Networks