A novel pipeline for adrenal tumour segmentation. (June 2018)

• Record Type: Journal Article
• Authors: Koyuncu, Hasan; Ceylan, Rahime; Erdogan, Hasan; Sivri, Mesut
• Is Part Of: Computer methods and programs in biomedicine. Volume 159(2018)
• Page Start: 77
• Page End: 86
• Publication Date: 2018-06
• Subjects: Adrenal tumour -- Arterial and portal phase -- Computed tomography -- Hybrid approach -- Statistical pipeline -- Tumour segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.01.032
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Segmentation of adherent or noncohesive tumours which can be unilateral or bilateral. Semi-automated and statistical approach transacting directly on the abdominal section of a CT scan. This process would be significantly more difficult than patch based approaches since it uses the whole image. Adrenal tumour segmentation including more than a single tumour type. A detailed study using more than 30 CT images. No efficient pipeline exists that is unaffected by shape and intensity features of different tumours (size, shape, and location variety, along with low contrast). Abstract: Background and objective: Adrenal tumours occur on adrenal glands surrounded by organs and osteoid. These tumours can be categorized as either functional, non-functional, malign, or benign. Depending on their appearance in the abdomen, adrenal tumours can arise from one adrenal gland (unilateral) or from both adrenal glands (bilateral) and can connect with other organs, including the liver, spleen, pancreas, etc. This connection phenomenon constitutes the most important handicap against adrenal tumour segmentation. Size change, variety of shape, diverse location, and low contrast (similar grey values between the various tissues) are other disadvantages compounding segmentation difficulty. Few studies have considered adrenal tumour segmentation, and no significant improvement has been achieved for unilateral, bilateral, adherent, or noncohesive tumour segmentation. There is also no recognised segmentation pipeline or method for adrenal tumours including different shape, size, or location information. Methods: This study proposes an adrenal tumour segmentation (ATUS) pipeline designed to eliminate the above disadvantages for adrenal tumour segmentation. ATUS incorporates a number of image methods, including contrast limited adaptive histogram equalization, split and merge based on quadtree decomposition, mean shift segmentation, large grey level eliminator, and region growing. Results: Performance assessment of ATUS was realised on 32 arterial and portal phase computed tomography images using six metrics: dice, jaccard, sensitivity, specificity, accuracy, and structural similarity index. ATUS achieved remarkable segmentation performance, and was not affected by the discussed handicaps, on particularly adherence to other organs, with success rates of 83.06%, 71.44%, 86.44%, 99.66%, 99.43%, and 98.51% for the metrics, respectively, for images including sufficient contrast uptake. Conclusions: The proposed ATUS system realises detailed adrenal tumour segmentation, and avoids known disadvantages preventing accurate segmentation. Graphical abstract: