Semantic video indexing

Arun Hampapur

doi:10.1145/309844.309883

Feature Normalization for Video Indexing and Retrieval

10.21236/ada459805 ◽

1996 ◽

Cited By ~ 1

Author(s):

Vikrant Kobla ◽

David Doermann ◽

King-Ip Lin ◽

Christos Faloutsos

Keyword(s):

Video Indexing ◽

Indexing And Retrieval ◽

Feature Normalization ◽

Video Indexing And Retrieval

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Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

Surgical Endoscopy ◽

10.1007/s00464-021-08381-6 ◽

2021 ◽

Author(s):

Daichi Kitaguchi ◽

Nobuyoshi Takeshita ◽

Hiroki Matsuzaki ◽

Hiro Hasegawa ◽

Takahiro Igaki ◽

...

Keyword(s):

Deep Learning ◽

Total Mesorectal Excision ◽

Rectal Wall ◽

Learning Model ◽

Video Indexing ◽

Video Data ◽

Transanal Total Mesorectal Excision ◽

Mesorectal Excision ◽

Purse String ◽

Deep Learning Model

Abstract Background Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. Methods This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. Results Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. Conclusions To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

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