title: Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.
abstract: This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p < 0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use.
authors.affiliation: Zhujiang Hospital, Southern Medical University; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. 530904236@qq.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Cai
authors.forename: Baochun
authors.initials: B
authors.lastname: He
authors.forename: Yingfang
authors.initials: Y
authors.lastname: Fan
authors.forename: Chihua
authors.initials: C
authors.lastname: Fang
authors.forename: Fucang
authors.initials: F
authors.lastname: Jia
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult