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• W4224440658 startingPage "102223" @default.
• W4224440658 abstract "Abdominal organ segmentation is the crucial research direction in computer assisted diagnostic systems. Segmentation of multiple organs in medical images is known as Multiorgan segmentation. It is a widespread subject of research in the realm of medical image analysis. The purpose of this study is to provide the comprehensive systematic literature review on segmentation of multiple organs in abdomen CT scans. This paper focuses on the progression of state-of-art methods from traditional techniques to deep learning models. Firstly, the methods are classified into three categories: atlas based, statistical shape models and deep learning models. Secondly, research is carried out to determine which organs require more attention. The liver, kidney, and spleen are the most often selected organs, whereas the esophagus, duodenum, and portal vein are rarely picked. When medical images are taken into account for research, datasets play a vital role. This paper sheds light on publicly available datasets along with their size, no of organ classes and, related challenges which make the current study more effective and useful for the researchers in the same field. Further, evaluation metrics along with their scope and characteristics are presented. We conclude with a discussion of challenges and future directions which will open pathways for researchers. Based on the surveyed research papers, Dense-Net came out as an optimal choice. Recently, the standard practice in multi organ segmentation is two step deep learning models in sequential manner, which can take leverage of two models." @default.
• W4224440658 created "2022-04-27" @default.
• W4224440658 creator A5010484649 @default.
• W4224440658 creator A5026348788 @default.
• W4224440658 creator A5029567791 @default.
• W4224440658 date "2022-07-01" @default.
• W4224440658 modified "2023-10-16" @default.
• W4224440658 title "Evolution of multiorgan segmentation techniques from traditional to deep learning in abdominal CT images – A systematic review" @default.
• W4224440658 cites W118753260 @default.
• W4224440658 cites W1489942343 @default.
• W4224440658 cites W1517839093 @default.
• W4224440658 cites W1522965957 @default.
• W4224440658 cites W1544547617 @default.
• W4224440658 cites W1547035658 @default.
• W4224440658 cites W1667869507 @default.
• W4224440658 cites W1680164276 @default.
• W4224440658 cites W17779984 @default.
• W4224440658 cites W18671957 @default.
• W4224440658 cites W1901129140 @default.
• W4224440658 cites W1901606657 @default.
• W4224440658 cites W1970928383 @default.
• W4224440658 cites W1975605357 @default.
• W4224440658 cites W1990109229 @default.
• W4224440658 cites W1992496678 @default.
• W4224440658 cites W2000585255 @default.
• W4224440658 cites W2007079295 @default.
• W4224440658 cites W2016974693 @default.
• W4224440658 cites W2018662705 @default.
• W4224440658 cites W2032377318 @default.
• W4224440658 cites W2035929909 @default.
• W4224440658 cites W2038952578 @default.
• W4224440658 cites W2042089741 @default.
• W4224440658 cites W2046622279 @default.
• W4224440658 cites W2048515486 @default.
• W4224440658 cites W2049247209 @default.
• W4224440658 cites W2049503784 @default.
• W4224440658 cites W2049516470 @default.
• W4224440658 cites W2050542229 @default.
• W4224440658 cites W2059961245 @default.
• W4224440658 cites W2069901410 @default.
• W4224440658 cites W2085091083 @default.
• W4224440658 cites W2098478186 @default.
• W4224440658 cites W2102595307 @default.
• W4224440658 cites W2107956652 @default.
• W4224440658 cites W2109315631 @default.
• W4224440658 cites W2113576511 @default.
• W4224440658 cites W2115167851 @default.
• W4224440658 cites W2115560616 @default.
• W4224440658 cites W2122632052 @default.
• W4224440658 cites W2135535334 @default.
• W4224440658 cites W2138047924 @default.
• W4224440658 cites W2142445683 @default.
• W4224440658 cites W2142456468 @default.
• W4224440658 cites W2143781397 @default.
• W4224440658 cites W2148347694 @default.
• W4224440658 cites W2149335720 @default.
• W4224440658 cites W2160153825 @default.
• W4224440658 cites W2162140684 @default.
• W4224440658 cites W2168747910 @default.
• W4224440658 cites W2180031803 @default.
• W4224440658 cites W2193350392 @default.
• W4224440658 cites W2244003928 @default.
• W4224440658 cites W2295427071 @default.
• W4224440658 cites W2301358467 @default.
• W4224440658 cites W2304189599 @default.
• W4224440658 cites W2319302553 @default.
• W4224440658 cites W2345010043 @default.
• W4224440658 cites W2395611524 @default.
• W4224440658 cites W2412453619 @default.
• W4224440658 cites W2413073178 @default.
• W4224440658 cites W2523005792 @default.
• W4224440658 cites W2555096873 @default.
• W4224440658 cites W2592929672 @default.
• W4224440658 cites W2613041730 @default.
• W4224440658 cites W2615034107 @default.
• W4224440658 cites W2615916349 @default.
• W4224440658 cites W2618530766 @default.
• W4224440658 cites W2738088905 @default.
• W4224440658 cites W2771252144 @default.
• W4224440658 cites W2791680898 @default.
• W4224440658 cites W2792124446 @default.
• W4224440658 cites W2804860796 @default.
• W4224440658 cites W2805957987 @default.
• W4224440658 cites W2883225196 @default.
• W4224440658 cites W2891994532 @default.
• W4224440658 cites W2921629474 @default.
• W4224440658 cites W2963150697 @default.
• W4224440658 cites W2977883299 @default.
• W4224440658 cites W3015802920 @default.
• W4224440658 cites W3071682211 @default.
• W4224440658 cites W3098715541 @default.
• W4224440658 cites W3127107873 @default.
• W4224440658 cites W3129128741 @default.
• W4224440658 cites W3162505601 @default.
• W4224440658 cites W3173443949 @default.
• W4224440658 cites W3216078403 @default.
• W4224440658 cites W38089536 @default.
• W4224440658 cites W4212875960 @default.

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