A review of the image segmentation and registration methods in liver motion correction in C-arm perfusion imaging

Haseljić, Hana; Frysch, Robert; Kulvait, Vojtech; Rose, Georg

doi:10.1109/icat47117.2019.8938844

Cited by 1 publication

(1 citation statement)

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“…The problem can be addressed using surface based registration and liver segmentation. 1 Image registration methods 2 are often designed to process medical images under the assumption of perfect acquisition conditions which guarantee that a full volume of a scanned organ is completely covered by a CT scan. In those cases, registered images overlap entirely and each point from one image can be mapped to a corresponding point in another image.…”

Section: Introductionmentioning

confidence: 99%

Deformable registration of 3D CT images with partial liver

Palma¹,

Padiasek²,

Sitek³

et al. 2023

Medical Imaging 2023: Image Processing

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Multiphase contrast-enhanced computed tomography (CT) is a popular method used by radiologists to diagnose patients with liver diseases. The analysis of multiphase images may involve image segmentation and registration. While the latter can be addressed using traditional registration techniques, it can be proven difficult when dealing with sub-optimal acquisitions.In real conditions, a registration process not only needs to accommodate patient's motion occurring between acquisitions but also the variation in the scanner field of view that changes between the acquisitions. Actually, each image acquisition acquired during an individual exam may cover different portions of the liver, resulting in full or partial liver scans. Partial liver scans present a challenge to registration methods that expect to see the same portion of the body in the input data. Such methods would tend to register partial liver volumes to full liver volumes, and as a result, misplace locations of internal liver landmarks.In this work we present a method that registers multiphase CT scans with partial and full liver while ignoring portions of liver that are represented only in one of the scans. We assess its performance and discuss its capacity to improve tracking of liver lesions across phases.

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Section: Introductionmentioning

confidence: 99%