Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training

查看全文 作　　者：Matthew [1]Grudza;Brandon [2]Salinel;Sarah [3]Zeien;Matthew [2,3]Murphy;Jake [4]Adkins;Corey T [5]Jensen;Curtis [6]Bay;Vikram [7]Kodibagkar;Phillip [2]Koo;Tomislav [8]Dragovich;Michael A [9]Choti;Madappa [8]Kundranda;Tanveer Syeda-[10]Mahmood;Hong-Zhi [10]Wang;John [2]Chang

机构地区：[1]School of Biological Health and Systems Engineering,Arizona State University,Tempe,AZ 85287,United States;[2]Department of Radiology,Banner MD Anderson Cancer Center,Gilbert,AZ 85234,United States;[3]School of Osteopathic Medicine,A.T.Still University,Mesa,AZ 85206,United States;[4]Department of Abdominal Imaging,MD Anderson Cancer Center,Houston,TX 77030,United States;[5]Department of Abdominal Imaging,University Texas MD Anderson Cancer Center,Houston,TX 77030,United States;[6]Department of Interdisciplinary Sciences,A.T.Still University,Mesa,AZ 85206,United States;[7]School of Biological and Health Systems Engineering,Arizona State University,Tempe,AZ 85287,United States;[8]Division of Cancer Medicine,Banner MD Anderson Cancer Center,Gilbert,AZ 85234,United States;[9]Department of Surgical Oncology,Banner MD Anderson Cancer Center,Gilbert,AZ 85234,United States;[10]IBM Almaden Research Center,IBM,San Jose,CA 95120,United States

出　　处：《World Journal of Radiology》索引2023年第15卷第12期,共11页

摘　　要：BACKGROUND Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle,making the lesions difficult to detect.Secondobserver is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence(AI).AIM To achieve appropriate AI model training,a large annotated dataset is necessary to train the AI models.Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth:Skip-slice annotation and AI-initiated annotation.METHODS We developed a 2D U-Net as an AI second observer for detecting colorectal cancer(CRC)and an ensemble of 5 differently initiated 2D U-Net for ensemble technique.Each model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis,tested with 7 cases,and validated with 20 cases from The Cancer Imaging Archive cases.The sensitivity,false positives per case,and estimated Dice coefficient were obtained for each method of training.We compared the two methods of annotations and the time reduction associated with the technique.The time differences were tested using Friedman’s two-way analysis of variance.RESULTS Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time(P<0.001).Reduction of up to 2/3 of the annotation does not reduce AI model sensitivity or false positives per case.Although initializing human annotation with AI reduces the annotation time,the reduction is minimal,even when using an ensemble AI to decrease false positives.CONCLUSION Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth. 关键词：Artificial intelligence Colorectal cancer Detection

相关文献

参考文献（34）
引证文献（0）
耦合文献（49）

参考文献(34)

耦合文献(49)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training

参考文献(34)

耦合文献(49)

用户提示