 |
PhaKIR - Challenge:
Phase, Keypoint and Instrument Recognition part of
|
|
|
PhaKIR - Challenge:
Phase, Keypoint and Instrument Recognition part of
|
|
PhaKIR - Challenge:
Phase, Keypoint and Instrument Recognition part of
|
ABOUT THE CHALLENGE
Accurate and reliable recognition and localization of surgical instruments in endoscopic video recordings is the basis for a variety of applications in computer- and robot-assisted minimally invasive surgery (RAMIS) [1]. The robust handling of real-world conditions such as varying illumination levels, blurred movement of the instruments and the camera, severe sudden bleeding that impairs the field of view, or even unexpected smoke development is an important prerequisite for such procedures. To process the information extracted from the endoscopic images in the best possible way, the inclusion of the context of the operation can be used as a promising possibility, which can be realized, for example, by knowing the current phase of an intervention.
In our EndoVis2024 subchallenge, we present a dataset for which three tasks are to be performed: Instance segmentation of the surgical instruments, keypoint estimation, and procedure phase recognition. The following annotations are available for this: pixel-accurate instance segmentations of surgical instruments together with their instrument types for a total of 19 categories, coordinates of relevant instrument keypoints (instrument tip(s), shaft-tip transition, shaft), and a classification of the phases of an intervention into eight different phase categories. Our dataset consists of 13 real-world videos of human cholecystectomies ranging from 23 to 60 minutes in duration. The procedures were performed by experienced physicians, and the videos were recorded in three hospitals. In addition to existing datasets, our annotations provide instance segmentations of surgical instruments, relevant keypoints, and intervention phases in one dataset and thus comprehensively cover instrument localization and the context of the operation. Furthermore, the provision of the complete video sequences offers the opportunity to include the temporal information regarding the respective tasks and thus further optimize the resulting methods and outcomes.
The structured description of the challenge design can be found here: PhaKIR_Structured_Description_of_Challenge_Design.pdf.
[1] T. Rueckert, D. Rueckert, and C. Palm, "Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art", Computers in Biology and Medicine, vol. 169, pp. 107929, 2024, DOI: https://doi.org/10.1016/j.compbiomed.2024.107929.
AWARDS
For each of the three tasks:
1st place with 500 €
2nd place with 300 €
3rd place with 200 €
Sponsored by
 |
ABOUT THE CHALLENGE
Accurate and reliable recognition and localization of surgical instruments in endoscopic video recordings is the basis for a variety of applications in computer- and robot-assisted minimally invasive surgery (RAMIS) [1]. The robust handling of real-world conditions such as varying illumination levels, blurred movement of the instruments and the camera, severe sudden bleeding that impairs the field of view, or even unexpected smoke development is an important prerequisite for such procedures. To process the information extracted from the endoscopic images in the best possible way, the inclusion of the context of the operation can be used as a promising possibility, which can be realized, for example, by knowing the current phase of an intervention.
In our EndoVis2024 subchallenge, we present a dataset for which three tasks are to be performed: Instance segmentation of the surgical instruments, keypoint estimation, and procedure phase recognition. The following annotations are available for this: pixel-accurate instance segmentations of surgical instruments together with their instrument types for a total of 19 categories, coordinates of relevant instrument keypoints (instrument tip(s), shaft-tip transition, shaft), and a classification of the phases of an intervention into eight different phase categories. Our dataset consists of 13 real-world videos of human cholecystectomies ranging from 23 to 60 minutes in duration. The procedures were performed by experienced physicians, and the videos were recorded in three hospitals. In addition to existing datasets, our annotations provide instance segmentations of surgical instruments, relevant keypoints, and intervention phases in one dataset and thus comprehensively cover instrument localization and the context of the operation. Furthermore, the provision of the complete video sequences offers the opportunity to include the temporal information regarding the respective tasks and thus further optimize the resulting methods and outcomes.
The structured description of the challenge design can be found here: PhaKIR_Structured_Description_of_Challenge_Design.pdf.
[1] T. Rueckert, D. Rueckert, and C. Palm, "Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art", Computers in Biology and Medicine, vol. 169, pp. 107929, 2024, DOI: https://doi.org/10.1016/j.compbiomed.2024.107929.
AWARDS
For each of the three tasks:
1st place with 500 €
2nd place with 300 €
3rd place with 200 €
Sponsored by
|