Automated pancreatic mass segmentation in computer tomography images using a voting ensemble method based on encoder-decoder architectures
Pancreatic cancer poses significant challenges in early diagnosis, with a high mortality rate of 98%, and is responsible for 4.7% of cancer deaths. Early diagnosis, mainly done by imaging exams, is the main factor that determines prognosis. While it is common to perform cascaded segmentation, the us...
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| Vydané v: | Procedia computer science Ročník 256; s. 1167 - 1174 |
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2025
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| Abstract | Pancreatic cancer poses significant challenges in early diagnosis, with a high mortality rate of 98%, and is responsible for 4.7% of cancer deaths. Early diagnosis, mainly done by imaging exams, is the main factor that determines prognosis. While it is common to perform cascaded segmentation, the use of ensemble strategies is rarely explored in the literature for pancreatic mass segmentation. In cascaded methods, the performance of the pancreatic mass segmentation step is also directly affected by the previous steps. In this paper we aim to study the impact of a localization step in three levels of precision while using an ensemble method to carry out pancreatic mass segmentation. A voting ensemble method is proposed that combine three encoder-decoder based networks, namely U-Net, Feature Pyramid Network (FPN) and LinkNet. The results obtained were competitive with existing literature, achieving a Dice Score of 63.89 ± 2.88% on the smallest resolution and 60.35 ± 4.86% on the biggest resolution on the MSD dataset. The results show that an ensemble method can mitigate the impact of previous steps in a cascaded method without significant loss in performance. |
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| AbstractList | Pancreatic cancer poses significant challenges in early diagnosis, with a high mortality rate of 98%, and is responsible for 4.7% of cancer deaths. Early diagnosis, mainly done by imaging exams, is the main factor that determines prognosis. While it is common to perform cascaded segmentation, the use of ensemble strategies is rarely explored in the literature for pancreatic mass segmentation. In cascaded methods, the performance of the pancreatic mass segmentation step is also directly affected by the previous steps. In this paper we aim to study the impact of a localization step in three levels of precision while using an ensemble method to carry out pancreatic mass segmentation. A voting ensemble method is proposed that combine three encoder-decoder based networks, namely U-Net, Feature Pyramid Network (FPN) and LinkNet. The results obtained were competitive with existing literature, achieving a Dice Score of 63.89 ± 2.88% on the smallest resolution and 60.35 ± 4.86% on the biggest resolution on the MSD dataset. The results show that an ensemble method can mitigate the impact of previous steps in a cascaded method without significant loss in performance. |
| Author | de Paiva, Anselmo Cardoso Araújo, Alexandre de Carvalho Júnior, Geraldo Braz de Almeida, João Dallyson Sousa |
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| Cites_doi | 10.1007/978-1-4842-6168-2_10 10.1016/j.jksuci.2023.01.014 10.1007/978-3-030-01228-1_15 10.1007/978-3-319-24574-4_28 10.1016/j.engappai.2022.105151 10.1109/ACCESS.2019.2913442 10.1109/VCIP.2017.8305148 10.1016/j.neucom.2022.10.060 10.1016/j.bspc.2023.105562 10.1016/j.neunet.2022.10.026 10.7150/thno.52508 10.1007/978-3-319-67558-9_28 10.1016/j.bspc.2022.103519 10.1016/j.patrec.2023.05.004 10.1109/CVPRW.2018.00051 10.1007/978-3-658-25326-4_7 10.1109/ICCV.2017.324 10.1016/j.media.2020.101884 10.1007/978-3-030-32245-8_24 10.1109/3DV.2019.00035 10.3390/cancers16020436 10.1038/s41598-022-07111-9 |
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| Keywords | Pancreatic mass Ensemble Model Encoder-Decoder architecture |
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