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  "fetched_at": "2026-10-24T13:02:45Z",
  "source_url": "https://www.kaggle.com/competitions/body-morphometry-for-sarcopenia/data",
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      "content": "<h1>데이터 설명</h1>\n\n<p>&nbsp&nbsp본 대회는 CT 데이터에서의 근육과 지방을 Segmentation하는 것이 목표입니다. 이를 위해 3D CT 데이터에서 임상적으로 가장 유의미한 정보를 나타내는 L3 Slice에서 Segmentation을 진행하며, Training 및 Test 데이터는 전문 임상의가 수동으로 선별한 데이터입니다.</p>\n<center><img src=\"https://drive.google.com/uc?export=download&id=1sdNiNLPpX7eHDIaXNoVq74AFA5AG7b1U\"></center>\n<p>&nbsp&nbspTrainig 및 Test 데이터는 다음과 같은 DCM format의 CT 데이터와 PNG format의 Label(Ground Truth) 형태의 데이터로 구성되어 있습니다.</p>\n<img src=\"https://drive.google.com/uc?export=download&id=13TaEt1zMpr6rjaPZkJb4G55OqI8g_75u\">\n\n<p>DCM format의 CT 영상은 512x512 사이즈로 되어 있으며, 일반적으로 -1024~3096의 HU 값을 포함합니다. 이를 그대로 Training에 사용하셔도 되지만, 256 Gray scale로 변환하여 Training 하는 것을 추천합니다. (선택은 자유입니다.) 또한, 다양한 HU to Grayscale 변환 방법이 존재하며, 대표적인 방법은 다음과 같습니다.</p>\n<img src=\"https://drive.google.com/uc?export=download&id=1IkvOXxF-OwYElYeHhDPkzuKedcnkIM2e\">\n\n<p>본 대회는 각 분야 전문가들이 사전 조사와 검증을 통해 총 100개의 Training 데이터를 선정하였습니다. 참가자 여러분들은 다양한 방법(전처리, Augmentation, 인공지능 모델 등)을 통해 자유롭게 성능 향상을 목표로 의료 인공지능 개발에 참여해주시기 바랍니다.</p>\n<img src=\"https://drive.google.com/uc?export=download&id=1T6WJ9YW5HWFv08edyvmD4S1JIRMhUi7j\">\n\n<h1>Data Citation</h1>\nAmber L. Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert J. S. Litjens, Bjoern H. Menze, Olaf Ronneberger, Ronald M. Summers, Patrick Bilic, Patrick Ferdinand Christ, Richard K. G. Do, Marc Gollub, Jennifer Golia-Pernicka, Stephan Heckers, William R. Jarnagin, Maureen McHugo, Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, and M. Jorge Cardoso. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. CoRR, abs/1902.09063, 2019. URL http://arxiv.org/abs/1902.09063.\n\n\n<h1>Data Licence</h1>\n\nAttribution-ShareAlike 4.0 International\n\n=======================================================================\n\nCreative Commons Corporation (\"Creative Commons\") is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses and related information available on an \"as-is\" basis. Creative Commons gives no warranties regarding its licenses, any material licensed under their terms and conditions, or any related information. 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      "content": "<h1>개요</h1>\n<p>&nbsp;&nbsp;MOAI 2020 Body Morphometry AI Segmentation Online Challenge는 원광대학교병원과 서울아산병원의 공동 연구 주제인 Body Morphometry 중 하나로, CT 영상에서 근육과 지방에 대하여 인공지능 기술을 기반으로 자동 Segmentation 기술 개발 및 정량적 평가 수행을 통한 의료 인공지능 연구 개발 활성화를 목표로 하고 있습니다.</p>\n\n<h2>Body Morphometry란?</h2>\n<p>&nbsp;&nbsp;모든 의료 및 건강증진의 근본이 되는 데이터로 Body Morphometry 관련 질환은 모든 질환 중 가장 높은 유병률을 나타내며 성인 인구의 1/3 이상의 사회적 문제입니다. 특히, 근육, 지방, 뼈의 변화는 많은 질병과 건강악화의 원인이자 결과로 그 중요성은 나날이 증가하고 있습니다.</p>\n\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5709871%2F919c6bd6cd3c587333d69ee4e4fddeef%2Fbody-morphometry-image.bmp?generation=1599720039565730&amp;alt=media\">\n\n\n<h2>Body Morphometry 중요성</h2>\n<p>&nbsp;&nbsp;세계 각국 질병구조가 만성병 구조로 바뀌고 고령화 사회로 진전됨에 따라 예방과 관리에 대한 중요성이 재인식 되고 있습니다. 이러한 만성질환은 발병원인이 복합적으로, 진단 및 치료의 어려움이 발생합니다. 이는 완치의 한계 발생과 오진율 증가의 원인이 되기도 합니다. 최근 이러한 문제점을 개선하기 위해 개인의료기록과 다양한 의료 데이터의 통합 분석을 통한 정밀 진단 및 치료, 예후예측을 통한 예방과 관리에 대한 연구가 급진전하고 있습니다. 특히, 비만, 근감소증, 골다공증과 같은 Body Morphometry 정보와 질병 간의 유의성에 관한 연구들이 보고되면서 다양한 의료 정보의 융합 연구에 대한 중요성과 필요성은 더욱 가중되고 있습니다.</p>\n\n<hr>\n\n<h1>대회목적</h1>\n<p>&nbsp;&nbsp;MOAI 2020 Body Morphometry AI Segmentation Challenge는 의료 인공지능 연구 개발 활성화를 목적으로 인공지능 학습 분위기 조성 및 확산, 혁신성장의 기회 마련을 위해 연구자 및 대학생들과 함께 아이디어 발굴을 위한 다음과 같은 세부 목표들을 가지고 있습니다.</p>\n\n- <h3>의료 데이터</h3>\n <p>&nbsp;&nbsp;의료 데이터는 관련 규정으로 인해 공유가 어려우며, 이해를 위한 추가적인 지식, Ground Truth 제작의 어려움 등의 특성으로 인해 접근성이 매우 떨어지는 데이터 중 한 가지입니다. 각 분야의 전문가들이 직접 선별하고 가공한 익명화(Anonymization)된 의료 데이터와 이에 해당하는 연구주제를 공유하여 관심 있는 연구자들이 의료 데이터와 해당 연구주제에 대한 접근을 용이하게 하고자 합니다.</p>\n\n- <h3>교류 활성화</h3>\n<p>&nbsp;&nbsp;의료 인공지능은 다양한 요소 기술들의 통합이 필요한 융합 기술 및 학제 간 연구입니다. 이러한 의료와 공학의 학제 간 연구는 다양한 전문지식이 필요하기 때문에 각 분야의 전문가 및 연구자들의 활발한 교류가 필수입니다. 본 대회는 해당 주제에 대한 정보/지식/데이터 공유에 한정되지 않고, 각 분야의 전문가와 연구자들 간의 교류를 지향하고 있습니다. 이를 통해 의료 인공지능 연구 활성화 계기를 만들고자 합니다.</p>\n\n- <h3>인공지능 인력 양성</h3>\n<p>&nbsp;&nbsp;현재 국내외 인공지능 전문가 및 연구자는 현저히 부족한 상황입니다. 본 대회는 전문 분야와 지식을 통한 실용적인 연구개발 주제를 다룸으로써 실무형 인공지능 인력 육성에 기여하고자 합니다. 또한, 의료 인공지능과 관련된 전처리, Augmentation, 후처리 등 기본적인 지식과 노하우를 공유하는 계기 마련을 통해 전문 인력의 질적·양적 양성의 가속화를 기반으로 국제 경쟁력 확보를 목표로 하고 있습니다.</p>\n\n<hr>\n\n<h1>참가자격</h1>\n- &nbsp;&nbsp;국내 대학(원)생, 일반인, 산학연 콘소시엄 등 (빅데이터에 관심 있는 모든 사람)\n- &nbsp;&nbsp;외국인의 경우, 국내 팀 소속으로 참여 가능",
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      "name": "Evaluation",
      "content": "<h1>평가규칙</h1>\n\n<h3>● DSC(Dice Similarity Coefficient)</h3>\n<p>&nbsp;&nbsp;정량적인 평가를 위해 Mean DSC를 사용합니다. 이는 인공지능을 이용하여 예측된 결과와 그에 상응하는 Ground Truth 간의 픽셀 단위 일치를 비교하는데 사용되며, 식은 아래와 같습니다.</p>\n<img width=\"100\" src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5709871%2Fb0d0bf65c03f9eea1b1f0b2d83748b95%2FDSC_Expression.PNG?generation=1599723633436913&amp;alt=media\">\n\n<p>&nbsp;&nbsp;여기서 X는 예측 분할 결과를 나타내며, Y는 Ground Truth를 나타냅니다. 리더보드의 점수(Leaderboard Score)는 각 영상에 대한 DSC의 평균을 나타냅니다.</p>\n\n<h3>● 리더보드(Leaderboard) 제출 형식</h3>\n<p>&nbsp;&nbsp;학습한 모델을 이용하여 테스트 데이터에 대한 분할 예측 결과를 아래 형식을 따르는 CSV 파일 형태로 저장하여 제출합니다.</p>\n<pre>1.구분자로 Comma(,) 문자를 사용하며, Comma(,) 문자 앞/뒤로 공백을 두지 않음\n2.첫번째 줄에는 Header 이름이 저장 됨 (헤더이름 : Id, EncodedPixels)\n3.두번째 줄 부터 각 Test 데이터에 대한 분할 예측 결과가 한 줄에 저장되며, 첫번째 열(ImageId)은 확장자를 제거한 테스트 데이터 파일 이름 및 레이블 번호, 두번째 열(EncodedPixels)은 분할 예측 결과가 저장\n4.분할 예측 결과는 제출 파일크기 감소를 위해 RLE(Run-length encoding)를 적용하여 저장\n\n※ ImageId 열 값 형식 : 파일이름_레이블번호\ncase101 데이터 예시 : case101_1 / case101_2 / case101_3\n※ RLE(Run-Length Encoding) : RLE는 동일한 문자가 이어서 반복되는 경우, 문자의 위치와 연속적으로 나타나는 횟수를 한 쌍으로 치환하여 압축하는 방법. 예를들어, ‘1 3 10 5’는 픽셀 1,2,3과 10,11,12,13,14이 분할 대상 레이블임을 표현\n</pre> \n <p>위 형식을 따르는 예시는 아래와 같으며, 'Data' 탭, 'Data Explorer'에 업로드된 코드를 확인바랍니다.</p>\n<pre>ImageId, EncodedPixels\ncase101_1,1 1 3 1…\ncase101_2,4 1 5 1…\ncase101_3,100 1 2 1…\ncase102_1,23 1…\ncase102_2,41 1…\ncase102_3,11 1…\n…\n</pre>\n\n<h3>● Training &amp; Test Code 및 Weight File</h3>\n<p>&nbsp;&nbsp;제출되는 학습/테스트 코드는 Kaggle Kernel에서 작업한 Jupyter Notebook/Python script만 허용되며, 제출 마감일 전에 최종 버전 스크립트를 주최자 ID에 공유해야합니다.\n 제출된 코드 버전은 Kaggle에서 에러 없이 실행되어야 하며, 테스트 결과 CSV 파일 및 Weight 파일이 모두 생성되어야합니다.</p>\n\n<h3>● Documentation</h3>\n<p>&nbsp;&nbsp;최종 제출 시, 아래 사항을 포함한 개발 문서를 PPT 형태로 제출하게 되며, 정성 평가에 사용됩니다.</p>\n\n<pre>[Title] 제목, 팀명, 팀원 전체 목록\n[Introduction] 적용한 기술 개요\n[Methods] 전처리, 후처리, 모델 블록도, 최적화 방법 등\n[Results] 결과\n</pre>",
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      "name": "rules",
      "content": "\n<h2>참가자격</h2>\n- 국내 대학(원)생, 일반인, 산학연 콘소시엄 등 (빅데이터에 관심 있는 모든 사람)\n- 외국인의 경우, 국내 팀 소속으로 참여 가능\n\n<h2>참가자당 한 개 계정만 참여 가능</h2>\n- 동일 참가자가 다수의 계정을 생성하여 본 대회에 참여할 수 없습니다.\n\n<h2>팀 외부, 공유 제한</h2>\n- 팀 외부에서 개인적으로 코드 또는 데이터를 공유하는 것은 허용되지 않습니다. \n- 모든 참가자가 사용할 수 있는 코드는 포럼을 통해 공유해도 됩니다.\n\n<h2>팀 합병</h2>\n- 팀 합병이 허용되며 팀 리더가 수행 할 수 있습니다. 병합하려면 결합 된 팀의 총 제출 수가 병합 날짜를 기준으로 허용되는 최대 값보다 작거나 같아야합니다. 허용되는 최대 값은 일일 제출 수에 대회가 진행된 일 수를 곱한 것입니다.\n\n<h2>팀 인원 수 제한</h2>\n- 팀 인원 수 제한 없음\n\n<h2>리더보드 제출 횟수 제한</h2>\n- 리더보드 제출 횟수는 하루에 5회로 제한됩니다.\n- 심사에 사용되는 최종 리더보드 제출은 1개만 선택해서 제출해야 합니다.\n\n<h2>최종 제출(중요)</h2>\n- <strong>Overview</strong> - <strong>Evaluation</strong>에 기재된 평가규칙 내용 참고\n\n<h2>제출 유의사항</h2>\n- 참가자는 아래 방법으로 최종 결과물을 대회 종료전 제출하여야 하며, 누락시 실격 사유가 될 수 있습니다.\n   1. 리더보드에 결과 1개를 선택하여 최종 제출\n   2. 리더보드에 최종 제출된 Jupyter notebook을 '**문의 및 제출 메일**'에 안내된 메일로 제출\n   3. 개발 문서를(PPT) '**문의 및 제출 메일**'에 안내된 메일로 제출\n   * 자세한 사항은 Overview-Evaluation 참조\n- Jupyter notebook 코드 제출은 Kaggle 상 에서 작성된 것만 허용됩니다.\n- Jupyter notebook Input 폴더에는 본 대회 주최자가 제공한 데이터, Output에는 Training & Test & Weight 파일이 반드시 포함되어야 합니다.   (Augmentation 수행 시, Augmentation 데이터도 함께 포함되어야 합니다.)\n- 제출된 Jupyter notebook 코드는 리더보드 점수를 복원할 수 있어야 합니다.\n- Jupyter notebook 코드는 오류 없이 실행되어야 합니다. (라이브러리 로딩 코드 포함)\n- 제출 결과를 검토하여 부정 제출 행위로 판단되는 경우, 실격될 수 있습니다.\n\n<h2>제출 메일</h2>\n- asan.moai.lab@gmail.com\n\n<h2>부정 제출</h2>\n본 대회 주최자는 공정한 대회 운영을 위해 대회 기간 중 부정 제출자를 대회에서 제재하고 있습니다.\n\n- 부정 제출(행위) \n일반적인 대회 규정에 의해서 인간이 예측한 값, 검증/평가 데이터셋 사용, 다른 참가자들과 형평성에 어긋나는 (의도적인)참가 방식 등은 모두 부정 제출로 간주 됩니다. 이러한 행위들은 실격사유가 될 수 있습니다.\n \n- 조치 방식\n우승 후보자의 소스코드를 대회 주최자가 대회 명세 및 준수사항을 모두 지켰는지, 분석 절차상 문제는 없는지, 분석 절차의 완결성은 얼마나 되는지 등에 대해서 평가를 진행하게 됩니다. 이 과정들을 통해서 부정행위자 및 미완결 코드 제출자는 실격처리 및 최종적인 랭킹에서 순위 변동 혹은 제외 될 수 있습니다.\n\n<h2>대회일정</h2>\n- 개최공고 : 09월 15일 (화) ~ 10월 11일 (일)\n- 제출기간 : 10월 12일 (월) 00:00 ~ 10월 19일 (월) 00:00\n- 심사발표 : 10월 26일 (월) 12:00\n- 시 상 식 : 10월 30일 (금)\n  (데이터셋 기반 코드 리뷰, 알고리즘 설명)\n\n<h2>상금</h2>\n- 전체 상금 : 500만원\n- 1등 : 300만원\n- 2등 : 150만원\n- 3등 :  50만원\n- 원광대학교 의료융합센터장명의의 상장 수여\n\n<h2>문의</h2>\n- asan.moai.lab@gmail.com 혹은 mcrc@wkuh.org",
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      "name": "Timeline",
      "content": "<h1>대회일정</h1>\n<pre>\n개최공고 : 09월 15일 (화) ~ 10월 11일 (일)\n제출기간 : 10월 12일 (월) 00:00 ~ 10월 19일 (월) 00:00\n심사발표 : 10월 26일 (월) 12:00\n시 상 식 : 10월 30일 (금)\n          (데이터셋 기반 코드 리뷰, 알고리즘 설명)\n</pre>\n",
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      "name": "Prizes",
      "content": "<h1>상금</h1>\n<pre>\n전체 상금 : 500만원\n1등 : 300만원\n2등 : 150만원\n3등 :  50만원\n원광대학교 의료융합센터장명의의 상장 수여\n</pre>",
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