revert challenge name

apps/openchallenges/challenge-service/src/main/resources/db/challenges.csv

@@ -422,7 +422,7 @@
 "421","curious2022","Brain shift with Intraoperative Ultrasound - Segmentation tasks","Brain shift with intraoperative ultrasound segmentation","Early brain tumor resection can effectively improve the patient’s survival rate. However, resection quality and safety can often be heavily affected by intra-operative brain tissue shift due to factors, such as gravity, drug administration, intracranial pressure change, and tissue removal. Such tissue shift can displace the surgical target and vital structures (e.g., blood vessels) shown in pre-operative images while these displacements may not be directly visible in the surgeon’s field of view.  Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools. Automatic algorithms for brain tissue segmentation in iUS, especially brain tumors and resection cavity can greatly facilitate the robustness and accuracy of brain shift correction through image registration, and allow easy interpretation of the iUS. This has the potential to improve surgical outcomes and patient survival rate.  The challenge is an ex...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/699/CuRIOUS_logo.png","https://curious2022.grand-challenge.org/","completed","intermediate","5","","2022-08-15","2022-09-13","2023-11-08 00:42:00","2023-11-15 21:50:06"
 "422","vessel-wall-segmentation-2022","Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis","Carotid vessel wall segmentation and diagnosis","In this challenge, the task is to segment the vessel wall from 3D-VISTA images and diagnose the atherosclerotic lesions with high accuracy and robustness. And then the clinical usable measurements such as wall thickness (difference between the lumen and outer wall contours), lumen area or stenosis percent can be derived from the vessel wall segmentation. In addition, the identification of the lumen and outer wall boundary of the vessel wall is also critical for the diagnosis of lesions. In summary, this challenge focuses on carotid vessel wall segmentation and atherosclerotic lesion diagnosis.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/700/%E5%9B%BE%E7%89%871.png","https://vessel-wall-segmentation-2022.grand-challenge.org/","completed","intermediate","5","","2022-07-08","2022-08-01","2023-11-08 00:42:00","2023-11-16 17:41:55"
 "423","crossmoda2022","Cross-Modality Domain Adaptation: Segmentation & Classification","CrossMoDA 2022: unsupervised domain adaptation","The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/701/squarelogo_2022.png","https://crossmoda2022.grand-challenge.org/","active","intermediate","5","","2022-05-11","\N","2023-11-08 00:42:00","2023-11-17 23:32:53"
-"424","atm22","Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)","Airway segmentation in x-ray CT for pulmonary diseases","Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/702/logo_xqf7twK.png","https://atm22.grand-challenge.org/","active","intermediate","5","https://doi.org/10.1007/978-3-031-16431-6_48","2022-08-17","\N","2023-11-08 00:42:00","2023-11-17 23:32:58"
+"424","atm22","Multi-site, Multi-Domain Airway Tree Modeling (ATM'22)","Airway segmentation in x-ray CT for pulmonary diseases","Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/702/logo_xqf7twK.png","https://atm22.grand-challenge.org/","active","intermediate","5","https://doi.org/10.1007/978-3-031-16431-6_48","2022-08-17","\N","2023-11-08 00:42:00","2023-11-21 17:16:40"
 "425","ps-fh-aop-2023","FH-PS-AOP challenge","Fetal head and pubic symphysis segmentation","The task of the FH-PS-AOP grand challenge is to automatically segment 700 FH-PSs from transperineal ultrasound images in the devised Set 2 (test set), given the availability of Set 1, consisting of 401 images.  Set 2 is held private and therefore not released to the potential participants to prevent algorithm tuning, but instead the algorithms have to be submitted in the form of Docker containers that will be run by organizers on Set 2. The challenge is organized by taking into account the current guidelines for biomedical image analysis competitions, in particular the recommendations of the Biomedical Image Analysis Challenges (BIAS) initiative for transparent challenge reporting.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/703/F2_WDBTbsq.tif","https://ps-fh-aop-2023.grand-challenge.org/","completed","intermediate","5","https://doi.org/10.1007/s11517-022-02747-1","2023-03-27","2023-09-20","2023-11-08 00:42:00","2023-11-16 17:41:56"
 "426","shifts","Shifts Challenge 2022","Shifts challenge 2022: distributional shift and uncertainty","The goal of the Shifts Challenge 2022 is to raise awareness among the research community about the problems of distributional shift, robustness, and uncertainty estimation, and to identify new solutions to address them. The competition will consist of two new tracks: White Matter Multiple Sclerosis (MS) lesion segmentation in 3D Magnetic Resonance Imaging (MRI) of the brain and Marine cargo vessel power estimation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/704/logo_1200.png","https://shifts.grand-challenge.org/","active","intermediate","5","https://arxiv.org/abs/2206.15407","2022-09-15","2024-04-08","2023-11-08 00:42:00","2023-11-17 23:33:07"
 "427","megc2022","ACMMM MEGC2022: Facial Micro-Expression Grand Challenge","Facial macro- and micro-expressions spotting","The unseen testing set (MEGC2022-testSet) contains 10 long video, including 5 long videos from SAMM (SAMM Challenge dataset) and 5 clips cropped from different videos in CAS(ME)3. The frame rate for SAMM Challenge dataset is 200fps and the frame rate for CAS(ME)3 is 30 fps. The participants should test on this unseen dataset.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/705/acmmm2022_logo.png","https://megc2022.grand-challenge.org/","active","intermediate","5","https://doi.org/10.1109/fg47880.2020.00029","2022-05-23","\N","2023-11-08 00:42:00","2023-11-16 17:39:17"