scholarly journals Surgical data science for next-generation interventions

2017 ◽  
Vol 1 (9) ◽  
pp. 691-696 ◽  
Author(s):  
Lena Maier-Hein ◽  
Swaroop S. Vedula ◽  
Stefanie Speidel ◽  
Nassir Navab ◽  
Ron Kikinis ◽  
...  
2021 ◽  
pp. 102306
Author(s):  
Lena Maier-Hein ◽  
Matthias Eisenmann ◽  
Duygu Sarikaya ◽  
Keno März ◽  
Toby Collins ◽  
...  

2018 ◽  
Vol 65 (11) ◽  
pp. 2649-2659 ◽  
Author(s):  
Sara Moccia ◽  
Sebastian J. Wirkert ◽  
Hannes Kenngott ◽  
Anant S. Vemuri ◽  
Martin Apitz ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lena Maier-Hein ◽  
Martin Wagner ◽  
Tobias Ross ◽  
Annika Reinke ◽  
Sebastian Bodenstedt ◽  
...  

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.


Author(s):  
Gregory D. Hager ◽  
Lena Maier-Hein ◽  
S. Swaroop Vedula

2021 ◽  
Vol 124 (2) ◽  
pp. 221-230
Author(s):  
Thomas M. Ward ◽  
Pietro Mascagni ◽  
Amin Madani ◽  
Nicolas Padoy ◽  
Silvana Perretta ◽  
...  

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Carolin M. Kobras ◽  
Andrew K. Fenton ◽  
Samuel K. Sheppard

AbstractMicrobiology is at a turning point in its 120-year history. Widespread next-generation sequencing has revealed genetic complexity among bacteria that could hardly have been imagined by pioneers such as Pasteur, Escherich and Koch. This data cascade brings enormous potential to improve our understanding of individual bacterial cells and the genetic basis of phenotype variation. However, this revolution in data science cannot replace established microbiology practices, presenting the challenge of how to integrate these new techniques. Contrasting comparative and functional genomic approaches, we evoke molecular microbiology theory and established practice to present a conceptual framework and practical roadmap for next-generation microbiology.


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