Heidelberg colorectal data set for surgical data science in the sensor operating room

AbstractImage-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.

Download Full-text

Surgical Data Science - from Concepts toward Clinical Translation

Medical Image Analysis ◽

10.1016/j.media.2021.102306 ◽

2021 ◽

pp. 102306

Author(s):

Lena Maier-Hein ◽

Matthias Eisenmann ◽

Duygu Sarikaya ◽

Keno März ◽

Toby Collins ◽

...

Keyword(s):

Data Science ◽

Clinical Translation ◽

Surgical Data Science ◽

Surgical Data

Download Full-text

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2018.2813015 ◽

2018 ◽

Vol 65 (11) ◽

pp. 2649-2659 ◽

Cited By ~ 22

Author(s):

Sara Moccia ◽

Sebastian J. Wirkert ◽

Hannes Kenngott ◽

Anant S. Vemuri ◽

Martin Apitz ◽

...

Keyword(s):

Data Science ◽

Surgical Data Science ◽

Surgical Data

Download Full-text

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Applied Sciences ◽

10.3390/app9153065 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3065 ◽

Cited By ~ 2

Author(s):

Dresp-Langley ◽

Ekseth ◽

Fesl ◽

Gohshi ◽

Kurz ◽

...

Keyword(s):

Big Data ◽

Data Science ◽

Computation Time ◽

Video Data ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Science Data ◽

Occam’S Razor ◽

Principle Of Parsimony ◽

Occam's Razor

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

Download Full-text