chore(openchallenges): 2024-06-17 DB update (#2715)

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

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

@@ -509,7 +509,7 @@
 "508","leopard","The LEOPARD Challenge","Uncover finer morphological features' prognostic value","Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024) to be able to predict the biochemical recurrence of prostate cancer.  Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/754/logo.png","https://leopard.grand-challenge.org/","active","5","","2024-04-10","2024-08-01","\N","2024-04-29 18:28:44","2024-05-20 16:38:34"
 "509","autopet-iii","AutoPET III","Refine the automated segmentation of tumor lesions in PET/CT scans","We invite you to participate in the third  autoPET Challenge. The focus of this year's challenge is to further refine the automated segmentation of tumor lesions in Positron Emission Tomography/Computed Tomography (PET/CT) scans in a multitracer multicenter setting.  Over the past decades, PET/CT has emerged as a pivotal tool in oncological diagnostics, management and treatment planning. In clinical routine, medical experts typically rely on a qualitative analysis of the PET/CT images, although quantitative analysis would enable more precise and individualized tumor characterization and  therapeutic decisions. A major barrier to clinical adoption  is  lesion segmentation, a necessary step for quantitative image analysis. Performed manually,  it's tedious, time-consuming and costly. Machine Learning offers the potential for fast and fully automated quantitative analysis of PET/CT images, as previously demonstrated in the first two autoPET challenges.  Building upon the insights gai...","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/755/autopet-2024.png","https://autopet-iii.grand-challenge.org/","upcoming","5","","2024-06-30","2024-09-15","\N","2024-04-29 18:29:47","2024-05-20 16:39:18"
 "510","ai4life-mdc24","AI4Life Microscopy Denoising Challenge","Unsupervised denoising of microscopy images","Wellcome to AI4Life-MDC24! In this challenge, we want to focus on an unsupervised denoising of microscopy images. By participating, researchers can contribute to a critical area of scientific research, aiding in interpreting microscopy images and potentially unlocking discoveries in biology and medicine.","https://rumc-gcorg-p-public.s3.amazonaws.com/logos/challenge/756/Challenge_square.png","https://ai4life-mdc24.grand-challenge.org/","active","5","","2024-05-04","2024-10-06","\N","2024-04-29 18:32:57","2024-05-20 16:39:01"
-"511","isles-24","Ischemic Stroke Lesion Segmentation Challenge 2024","ischemic stroke prediction","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","2024-05-20 16:39:42"
+"511","isles-24","Ischemic Stroke Lesion Segmentation Challenge 2024","ischemic stroke prediction","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/","active","5","","2024-06-15","2024-08-15","\N","2024-04-29 18:34:37","2024-05-20 16:39:42"
 "512","toothfairy2","ToothFairy2: Multi-Structure Segmentation in CBCT Volumes","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","2024-05-20 16:40:11"
 "513","pengwin","Pelvic Bone Fragments with Injuries Segmentation Challenge","Pelvic fractures characterization","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","2024-05-20 16:40:28"
 "514","aortaseg24","Multi-Class Segmentation of Aortic Branches and Zones in CTA","Aorta medical imaging","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","2024-05-20 16:41:36"