Overview: We ask for participation in the DBTex2 Grand Challenge by submitting algorithms for the detection of breast lesions on digital breast tomosynthesis images. The results of the competition will be announced at the Grand Challenges Symposium session of the 2021 AAPM Annual Meeting.
Organizers: This challenge is organized by the Duke Center for Artificial Intelligence in Radiology (DAIR) in collaboration with the SPIE-AAPM-NCI Grand Challenges Commitee.
Prizes: The winning team will receive $1000 prize sponsored by Duke Center for Artificial Intelligence in Radiology. Additionally, an individual from each of the two top-performing teams will receive a waiver of the meeting registration fee in order to present their methods during the SPIE Medical Imaging Conference.
Publication: Members of top teams will be invited to prepare and co-author a joint publication on this competition alongside the competition organizers, detailing the challenge itself, the methods used by the participants, and the corresponding results.
Note: The participants can visit https://www.reddit.com/r/DukeDBTData/ for additional advice and discussion.
Formatting the submission file:
The output of your method submitted to the evaluation system should be a single CSV file with the following columns:
PatientID,StudyUID,View,X,Width,Y,Height,Z,Depth,Score ID1,UID1,RLL,X(int),Width(int),Y(int),Height(int),Z(int),Depth(int),Score(float) ID2,UID2,LCC,X(int),Width(int),Y(int),Height(int),Z(int),Depth(int),Score(float) ID3,UID3,RMLO,X(int),Width(int),Y(int),Height(int),Z(int),Depth(int),Score(float) ID4,UID4,LMLO,X(int),Width(int),Y(int),Height(int),Z(int),Depth(int),Score(float)
Coordinates of the predicted bounding boxes should be given for the correct image orientation. In the official competition GitHub repository, we provide a python function for loading image data from a DICOM file into 3D array of pixel values.
Each entry (row) in the submission file must correspond to exactly one predicted bounding box. There may be arbitrary number of predicted bounding boxes for each DBT volume. It is not required to have predictions for all DBT volumes.
Example submission file will be provided on the competition website.
Definition of a true-positive detection
A predicted box is going to be counted as a true positive if the distance in pixels in the original image between its center point and the center of a ground truth box is less than half of its diagonal or 100 pixels, whichever is larger.
In terms of the third dimension, the ground truth bounding box is assumed to span 25% of volume slices before and after the ground truth center slice and the predicted box center slice is required to be included in this range to be considered a true positive.
The overall performance will be assessed as the average sensitivity for 1, 2, 3, and 4 FP/s per volume. The competition performance will be assessed only on studies containing a biopsied lesion.
Participants may use the training set cases in any manner they would like for the purpose of training their systems (consistent with the data license); there will be no restrictions on the advice sought from local experts for training purposes. The participants can also combine the provided training data with other data if they disclose that in the description of the algorithm.
The validation set and test sets cases, however, are to be manipulated, processed, and analyzed without human intervention.
Participants are free to download the training set and, subsequently, the validation and test sets when these datasets become available. It is important to note that once participants submit their test set output to the challenge organizers, they will be considered fully vested in the challenge, so that their performance results (without links to the identity of the participant) will become part of any presentations, publications, or subsequent analyses derived from the challenge at the discretion of the organizers.
The submission of test set output will not be considered complete unless it is accompanied by (1) a public GitHub repository that contains fully documented and reproducible code for your team's method (see the Overview page for the deadline) and (2) an agreement to be acknowledged in the Acknowledgment section (by name and institution—but without any link to the performance score of your particular method) of any manuscript that results from the challenge.
Members of top teams will be invited to prepare and co-author a joint publication on this competition alongside the competition organizers. This manuscript will describe the details of the challenge itself, as well as the methods used by the participants and the corresponding results. The representatives of the invited teams will be expected to provide descriptions of their algorithms and participate in the analysis of results and the preparation of the manuscript. However, please not that this is not a condition to participate.
Challenges are designed to motivate and reward novel computational approaches to a defined task. The use of commercial software (unless your group is affiliated with the organization that holds intellectual property rights to that software) or open source software (unless your group has a recognized association with the creation of that software) is not allowed, unless you can clearly demonstrate an innovative use, alteration, or enhancement to the application of such software.
Participation in the DBTex Challenges acknowledges the educational, friendly competition, and community-building nature of the challenges and commits to conduct consistent with this spirit for the advancement of the medical imaging research community. See this article for a discussion of lessons learned from the LUNGx Challenge, also sponsored by SPIE, AAPM, and NCI.
By participating in this challenge, each participant agrees to:
Start: May 24, 2021, midnight
Start: June 15, 2021, midnight
Start: July 11, 2021, midnight
July 21, 2021, midnight
You must be logged in to participate in competitions.Sign In