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README.md
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# Merlin Plus
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This repository presents Merlin Plus, a series of per-voxel annotations for organs and other anatomical structures in the Merlin dataset (Stanford). The per-voxel annotations were created by AI models trained on more than 14,000 CT scans at Johns Hopkins University.
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The Melrin Plus annotations were created as part of a collaboration between the Merlin Project at Stanford and the R-Super Project at Johns Hopkins University. R-Super is a novel AI training methodology that uses radiology reports and organ annotations to significantly improve tumor segmentation AI. Merlin Plus makes R-Super easily reproducible for the medical AI community.
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Merlin Plus includes per-voxel annotations for organs, blood vessels, organ parts (liver and pancreas sub-segments), and ducts:
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<details>
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<summary style="margin-left: 25px;">Class List</summary>
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<div style="margin-left: 25px;">
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```
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adrenal gland left
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adrenal gland right
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aorta
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bladder
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cbd stent
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celiac artery
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celiac trunk
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colon
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common bile duct
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duodenum
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esophagus
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femur left
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femur right
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gall bladder
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hepatic vessels
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intestine
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kidney left
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kidney right
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liver
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liver segment 1
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liver segment 2
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liver segment 3
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liver segment 4
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liver segment 5
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liver segment 6
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liver segment 7
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liver segment 8
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lung left
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lung right
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pancreas body
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pancreas head
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pancreas tail
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pancreas
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pancreatic duct
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portal vein and splenic vein
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postcava
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prostate
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rectum
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renal vein left
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renal vein right
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spleen
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stomach
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superior mesenteric artery
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superior mesenteric vein
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```
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</details>
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Merlin Plus presents annotations for the CT scans in the original Merlin dataset. To download these CT scans, please visit this website: https://stanfordaimi.azurewebsites.net/datasets/60b9c7ff-877b-48ce-96c3-0194c8205c40
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### Merlin Dataset
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Merlin Abdominal CT Dataset is an abdominal CT dataset consisting of 25,494 scans from 18,317 patients. Each scan is paired with its corresponding radiology report. The dataset includes abdominal and pelvis CT exams conducted between 2012 and 2018 at the Stanford Hospital Emergency Department, selected using CPT codes (72192, 72193, 72194, 74150, 74160, 74170, 74176, 74177, and 74178) through the STARR tool. For each exam, the DICOM series with the largest slice count was converted into NIfTI format, compressing the scans and removing patient-identifiable metadata.
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