chore(openchallenges): 2024-05-17 DB update (#2685)

Co-authored-by: vpchung <[email protected]>

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

@@ -512,7 +512,7 @@
 "511","isles-24","Ischemic Stroke Lesion Segmentation Challenge 2024","","Clinical decisions regarding the treatment of ischemic stroke patients depend on the accurate estimation of core (irreversibly damaged tissue) and penumbra (salvageable tissue) volumes (Albers et al. 2018). The clinical standard method for estimating perfusion volumes is deconvolution analysis, consisting of i) estimating perfusion maps through perfusion CT (CTP) deconvolution and ii) thresholding the perfusion maps (Lin et al. 2016). However, the different deconvolution algorithms, their technical implementations, and the variable thresholds used in software packages significantly impact the estimated lesions (Fahmi et al. 2012). Moreover, core tissue tends to expand over time due to irreversible damage of penumbral tissue, with infarct growth rates being patient-specific and dependent on diverse factors such as thrombus location and collateral circulation. Understanding the core's growth rate is clinically crucial for assessing the relevance of transferring a patient to a compre...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/757/ISLES24_1_c8Cz4NN.png","https://isles-24.grand-challenge.org/","upcoming","5","","2024-06-15","2024-08-15","\N","2024-04-29 18:34:37","2024-04-29 18:35:57"
 "512","toothfairy2","ToothFairy2: Multi-Structure Segmentation in CBCT Volumes","","This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Radboud University Medical Center. The challenge is hosted by grand-challenge and is part of MICCAI2024.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/759/GrandChallenge-Logo.png","https://toothfairy2.grand-challenge.org/","upcoming","5","","2024-06-30","2024-08-16","\N","2024-04-29 18:36:08","2024-04-29 18:36:51"
 "513","pengwin","Pelvic Bone Fragments with Injuries Segmentation Challenge","","Pelvic fractures, typically resulting from high-energy traumas, are among the most severe injuries, characterized by a disability rate over 50% and a mortality rate over 13%, ranking them as the deadliest of all compound fractures. The complexity of pelvic anatomy, along with surrounding soft tissues, makes surgical interventions especially challenging. Recent years have seen a shift towards the use of robotic-assisted closed fracture reduction surgeries, which have shown improved surgical outcomes. Accurate segmentation of pelvic fractures is essential, serving as a critical step in trauma diagnosis and image-guided surgery. In 3D CT scans, fracture segmentation is crucial for fracture typing, pre-operative planning for fracture reduction, and screw fixation planning. For 2D X-ray images, segmentation plays a vital role in transferring the surgical plan to the operating room via registration, a key step for precise surgical navigation.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/760/PENGWIN_qZTjVoC.jpg","https://pengwin.grand-challenge.org/","active","5","","2024-05-14","2024-07-31","\N","2024-04-29 18:37:01","2024-04-29 18:37:59"
-"514","aortaseg24","Multi-Class Segmentation of Aortic Branches and Zones in CTA","","3D Segmentation of Aortic Branches and Zones on Computed Tomography Angiography (CTA)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/761/Grand_Challenge_Logo.png","https://aortaseg24.grand-challenge.org/","upcoming","5","","2024-05-16","2024-08-16","\N","2024-04-29 18:38:07","2024-04-29 18:38:48"
+"514","aortaseg24","Multi-Class Segmentation of Aortic Branches and Zones in CTA","","3D Segmentation of Aortic Branches and Zones on Computed Tomography Angiography (CTA)","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/761/Grand_Challenge_Logo.png","https://aortaseg24.grand-challenge.org/","active","5","","2024-05-16","2024-08-16","\N","2024-04-29 18:38:07","2024-04-29 18:38:48"
 "515","aims-tbi","Automated Identification of Mod-Sev TBI Lesions","","Moderate to Severe Traumatic Brain Injury (msTBI) is caused by external forces (eg: traffic accidents, falls, sports) causing the brain to move rapidly within the skull, resulting in complex pathophysiological changes. Multiple primary, secondary, and surgery related processes has the potential to cause structural deformation in the brain. Each patient with msTBI has a unique accumulation of these structural changes, contributing to extremely heterogeneous lesions, considered a hallmark of msTBI (Covington & Duff, 2021). These lesions differ from other common brain pathologies (stroke, MS, brain tumor) in that they can be both focal or diffuse, varying in size, number and laterality, extending through multiple tissue types (GM/WM/CSF), and can also occur in homologous regions of both hemispheres. Lesions such as these can complicate image registration, normalization, and are known to introduce both local and global errors in brain parcellation (Diamond et al., 2020; King et al., 2...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/762/AIMS-TBI_logo_G50gkm9.png","https://aims-tbi.grand-challenge.org/","active","5","","2024-05-15","2024-08-16","\N","2024-04-29 18:38:56","2024-04-29 18:39:42"
 "516","brats-2024","BraTS 2024","","The International Brain Tumor Segmentation (BraTS) challenge. BraTS, since 2012, has focused on the generation of a benchmarking environment and dataset for the delineation of adult brain gliomas. The focus of this year''s challenge remains the generation of a common benchmarking environment, but its dataset is substantially expanded to ~4,500 cases towards addressing additional i) populations (e.g., sub-Saharan Africa patients), ii) tumors (e.g., meningioma), iii) clinical concerns (e.g., missing data), and iv) technical considerations (e.g., augmentations). Specifically, the focus of BraTS 2023 is to identify the current state-of-the-art algorithms for addressing (Task 1) the same adult glioma population as in the RSNA-ANSR-MICCAI BraTS challenge, as well as (Task 2) the underserved sub-Saharan African brain glioma patient population, (Task 3) intracranial meningioma, (Task 4) brain metastasis, (Task 5) pediatric brain tumor patients, (Tasks 7 & 8) global & local missing data, (...","","https://www.synapse.org/brats2024","active","1","","2024-03-01","2024-07-31","\N","2024-05-15 16:16:52","2024-05-15 16:19:18"
 "517","midi-b","Medical Image De-Identification Benchmark (MIDI-B) Challenge","","Image de-identification is a requirement for the public sharing of medical images. The goal of the Medical Image De-Identification Benchmark (MIDI-B) challenge is to assess rule-based DICOM image de-identification (deID) algorithms using a large and diverse set of standardized clinical images with synthetic identifiers. Automated image de-identification methods that preserve the research utility of the data are desirable.","","https://www.synapse.org/#!Synapse:syn53065760/wiki/625274","active","1","","2024-05-10","2024-09-06","\N","2024-05-15 16:20:50","2024-05-15 16:22:45"