Hyeongjin Lim, Yongha Gi, Yousun Ko, Yunhui Jo, Jinyoung Hong, Jonghyun Kim, Sung Hwan Ahn, Hee-Chul Park, Haeyoung Kim, Kwangzoo Chung, Myonggeun Yoon
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The generalized dataset comprised 1203 publicly available multi-scanner data. The device-dependent dataset comprised 1253 data, including the 1203 multi-CT scanner data and 50 single CT scanner data. Using these datasets, the generalized-dataset-based model (GDSM) and the device-dependent-dataset-based model (DDSM) were trained. The models were trained using nnU-Net and tested on ten data samples from a single CT scanner. The evaluation metrics included the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD), which were used to assess the overall performance of the models. In addition, DSC<sub>diff</sub>, HD<sub>ratio</sub>, and ASSD<sub>ratio</sub>, which are variations of the three existing metrics, were used to compare the performance of the models across different organs.</p>\n </section>\n \n <section>\n \n <h3> Result</h3>\n \n <p>For the average DSC, the GDSM and DDSM had values of 0.9251 and 0.9323, respectively. For the average HD, the GDSM and DDSM had values of 10.66 and 9.139 mm, respectively; for the average ASSD, the GDSM and DDSM had values of 0.8318 and 0.6656 mm, respectively. Compared with the GDSM, the DDSM showed consistent performance improvements of 0.78%, 14%, and 20% for the DSC, HD, and ASSD metrics, respectively. In addition, compared with the GDSM, the DDSM had better DSC<sub>diff</sub> values in 14 of 21 tested organs, better HD<sub>ratio</sub> values in 13 of 21 tested organs, and better ASSD<sub>ratio</sub> values in 14 of 21 tested organs. The three averages of the variant metrics were all better for the DDSM than for the GDSM.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The results suggest that combining the generalized dataset with a single scanner dataset resulted in an overall improvement in model performance for that device image.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2375-2383"},"PeriodicalIF":3.2000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A device-dependent auto-segmentation method based on combined generalized and single-device datasets\",\"authors\":\"Hyeongjin Lim, Yongha Gi, Yousun Ko, Yunhui Jo, Jinyoung Hong, Jonghyun Kim, Sung Hwan Ahn, Hee-Chul Park, Haeyoung Kim, Kwangzoo Chung, Myonggeun Yoon\",\"doi\":\"10.1002/mp.17570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Although generalized-dataset-based auto-segmentation models that consider various computed tomography (CT) scanners have shown great clinical potential, their application to medical images from unseen scanners remains challenging because of device-dependent image features.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>This study aims to investigate the performance of a device-dependent auto-segmentation model based on a combined dataset of a generalized dataset and single CT scanner dataset.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Method</h3>\\n \\n <p>We constructed two training datasets for 21 chest and abdominal organs. The generalized dataset comprised 1203 publicly available multi-scanner data. The device-dependent dataset comprised 1253 data, including the 1203 multi-CT scanner data and 50 single CT scanner data. Using these datasets, the generalized-dataset-based model (GDSM) and the device-dependent-dataset-based model (DDSM) were trained. The models were trained using nnU-Net and tested on ten data samples from a single CT scanner. The evaluation metrics included the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD), which were used to assess the overall performance of the models. In addition, DSC<sub>diff</sub>, HD<sub>ratio</sub>, and ASSD<sub>ratio</sub>, which are variations of the three existing metrics, were used to compare the performance of the models across different organs.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Result</h3>\\n \\n <p>For the average DSC, the GDSM and DDSM had values of 0.9251 and 0.9323, respectively. For the average HD, the GDSM and DDSM had values of 10.66 and 9.139 mm, respectively; for the average ASSD, the GDSM and DDSM had values of 0.8318 and 0.6656 mm, respectively. Compared with the GDSM, the DDSM showed consistent performance improvements of 0.78%, 14%, and 20% for the DSC, HD, and ASSD metrics, respectively. In addition, compared with the GDSM, the DDSM had better DSC<sub>diff</sub> values in 14 of 21 tested organs, better HD<sub>ratio</sub> values in 13 of 21 tested organs, and better ASSD<sub>ratio</sub> values in 14 of 21 tested organs. 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A device-dependent auto-segmentation method based on combined generalized and single-device datasets
Background
Although generalized-dataset-based auto-segmentation models that consider various computed tomography (CT) scanners have shown great clinical potential, their application to medical images from unseen scanners remains challenging because of device-dependent image features.
Purpose
This study aims to investigate the performance of a device-dependent auto-segmentation model based on a combined dataset of a generalized dataset and single CT scanner dataset.
Method
We constructed two training datasets for 21 chest and abdominal organs. The generalized dataset comprised 1203 publicly available multi-scanner data. The device-dependent dataset comprised 1253 data, including the 1203 multi-CT scanner data and 50 single CT scanner data. Using these datasets, the generalized-dataset-based model (GDSM) and the device-dependent-dataset-based model (DDSM) were trained. The models were trained using nnU-Net and tested on ten data samples from a single CT scanner. The evaluation metrics included the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD), which were used to assess the overall performance of the models. In addition, DSCdiff, HDratio, and ASSDratio, which are variations of the three existing metrics, were used to compare the performance of the models across different organs.
Result
For the average DSC, the GDSM and DDSM had values of 0.9251 and 0.9323, respectively. For the average HD, the GDSM and DDSM had values of 10.66 and 9.139 mm, respectively; for the average ASSD, the GDSM and DDSM had values of 0.8318 and 0.6656 mm, respectively. Compared with the GDSM, the DDSM showed consistent performance improvements of 0.78%, 14%, and 20% for the DSC, HD, and ASSD metrics, respectively. In addition, compared with the GDSM, the DDSM had better DSCdiff values in 14 of 21 tested organs, better HDratio values in 13 of 21 tested organs, and better ASSDratio values in 14 of 21 tested organs. The three averages of the variant metrics were all better for the DDSM than for the GDSM.
Conclusion
The results suggest that combining the generalized dataset with a single scanner dataset resulted in an overall improvement in model performance for that device image.
期刊介绍：
Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments
Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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