Multiphase Focal Liver Lesions Classification with Combined N-gram and BoVW

Huang, Huimin; Ji, Zhiyuan; Lin, Lanfen; Liao, Ziliang; Chen, Qingqing; Hu, Hongjie; Zhang, Qiaowei; Iwamoto, Yutaro; Chen, Yen‐Wei; Tong, Ruofeng; Wu, Jian

doi:10.1007/978-981-13-8566-7_8

Cited by 1 publication

(2 citation statements)

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“…The researchers have investigated various segmentation techniques. One of the oldest methods is manual tumor delineation by the radiologist, adopted in many CAD systems 4–13 . However, it is tedious, subjective, and impractical when the volume of cases is high.…”

Section: Related Workmentioning

confidence: 99%

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Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K‐means clustering

Pattwakkar

Kamath

Nanjundappa

et al. 2022

Int J Imaging Syst Tech

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Liver and liver tumor segmentations are essential in computer-aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a finetuned SegNet model and the liver tumor was segmented using the K-means clustering method coupled with a power-law transformation-based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.

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Section: Related Workmentioning

confidence: 99%

“…One of the oldest methods is manual tumor delineation by the radiologist, adopted in many CAD systems. [4][5][6][7][8][9][10][11][12][13] However, it is tedious, subjective, and impractical when the volume of cases is high. The other methods are either semi-/fully-automatic.…”

Section: Related Workmentioning

confidence: 99%