A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images

Ivashchenko, Oleksandra; Rijkhorst, Erik-Jan; Beek, Leon C. ter; Hoetjes, Nikie; Pouw, Bas; Nijkamp, Jasper; Kuhlmann, Koert F. D.; Ruers, Theo J.M.

doi:10.1016/j.mri.2019.12.008

Cited by 20 publications

(22 citation statements)

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“…In previous work by our group [13], a segmentation algorithm for multiphase MRI scanning of the liver was developed and incorporated into a custom “Liver segmentation pilot” extension of the 3D slicer [14], and is available to download at the GitHub opensource platform [15]. This extension allows for fully automated extraction of the liver contour from a multiphase image and involves 3 dynamic MRI phases using 4D k-means clustering and geodesic contour refinement.…”

Section: Methodsmentioning

confidence: 99%

“…The hepatic and portal vasculature, hepatic artery, and biliary ducts are segmented from various phases of the scan according to their size (i.e., the minimum and maximum vessel diameter) and characteristic contrast (i.e., positive or negative contrast), using a Hessian-based vessel-enhancing filter. This step can be performed using a readily available VMTK extension of the 3D slicer or the “Vesselness calculation” module of the “Liver segmentation pilot” extension described in our previous work [13], using the segmentation parameters listed in Table 1 (for more details on the effect that various vesselness parameters may have on the results of the segmentation, please consult [13] and [16]).…”

Section: Methodsmentioning

confidence: 99%

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Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy

et al. 2020

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Knowledge of patient-specific liver anatomy is key to patient safety during major hepatobiliary surgery. Three-dimensional (3D) models of patient-specific liver anatomy based on diagnostic MRI images can provide essential vascular and biliary anatomical insight during surgery. However, a method for generating these is not yet publicly available. This paper describes how these 3D models of the liver can be generated using open source software, and then subsequently integrated into a sterile surgical environment. The most common image quality aspects that degrade the quality of the 3D models as well possible ways of eliminating these are also discussed. Per patient, a single diagnostic multiphase MRI scan with hepatospecific contrast agent was used for automated segmentation of liver contour, arterial, portal, and venous anatomy, and the biliary tree. Subsequently, lesions were delineated manually. The resulting interactive 3D model could be accessed during surgery on a sterile covered tablet. Up to now, such models have been used in 335 surgical procedures. Their use simplified the surgical treatment of patients with a high number of liver metastases and contributed to the localization of vanished lesions in cases of a radiological complete response to neoadjuvant treatment. They facilitated perioperative verification of the relationship of tumors and the surrounding vascular and biliary anatomy, and eased decision-making before and during surgery.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy

et al. 2020

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“…All 3D models of the liver were created based on diagnostic Gd-EOB-DTPA-enhanced (Primovist® In Europe, Eovist® in the USA, Bayer Healthcare, Germany) 3D FFE-mDixon multiphase MRI-scans acquired not earlier than 4 weeks prior to the surgery. 7 Image acquisition contained five consecutive phases acquired during early enhancement of the contrast agent (i.e., pre-contrast, early arterial, late arterial, For each patient a detailed patient-specific 3D model of the liver, portal and hepatic veins, biliary tree and target tumors was extracted from Magnetic Resonance Images (MRI) of the liver. In phase II of the study, a 3D model of the ribs was added to the model as well, using diagnostic CT.…”

Section: Preoperative Mri and 3d Model Of The Livermentioning

confidence: 99%

“…4 This detailed plan is developed in consideration of the tumors' locations with respect to major blood vessels and biliary anatomy. [5][6][7] Despite the extensive pre-operative resection planning, the resection itself is still primarily based on the surgeon's recollection of the preoperative images, intraoperative tactile feedback and its correlation with live 2D ultrasound images. This lack of detailed imaging information during the procedure increases probability of intra-or post-operative hepatic complications, which occur in up to 23% of open liver resections, 8,9 while up to 15% of procedures result in irradical resections.…”

Section: Introductionmentioning

confidence: 99%

CBCT‐based navigation system for open liver surgery: Accurate guidance toward mobile and deformable targets with a semi‐rigid organ approximation and electromagnetic tracking of the liver

et al. 2021

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Purpose: The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ motion. This work investigates the accuracy of intraoperative surgical guidance during open liver resection, with a semi-rigid organ approximation and electromagnetic tracking of the target area. Methods: The suggested navigation technique incorporates a preoperative 3D liver model based on diagnostic 4D MRI scan, intraoperative contrast-enhanced CBCT imaging and electromagnetic (EM) tracking of the liver surface, as well as surgical instruments, by means of six degrees-of-freedom micro-EM sensors. Results: The system was evaluated during surgeries with 35 patients and resulted in an accurate and intuitive real-time visualization of liver anatomy and tumor's location, confirmed by intraoperative checks on visible anatomical landmarks. Based on accuracy measurements verified by intraoperative CBCT, the system's average accuracy was 4.0 ± 3.0 mm, while the total surgical delay due to navigation stayed below 20 min. Conclusions: The electromagnetic navigation system for open liver surgery developed in this work allows for accurate localization of liver lesions and critical anatomical structures surrounding the resection area, even when the liver was manipulated. However, further clinically integrating the method requires shortening the guidance-related surgical delay, which can be achieved by shifting to faster intraoperative imaging like ultrasound. Our approach is adaptable to navigation on other mobile and deformable organs, and therefore may benefit various clinical applications.

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“…In this research we applied this technique especially for the intrahepatic biliary duct diameter, which we noticed that the Contour Segmentation Technique was not used before to perform such kind of functions. In [36] the CST technique was applied with 4D models for liver anatomy in order to perform the surgical resection properly and to avoid any unusual anatomy related to the needed information about the biliary during the surgery. Such kind of information is not available via CT images nor via MRI.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

Aloudat¹,

Oudat²,

Migdady³

et al. 2020

Preprint

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A set of tubes known as bile ducts connects the liver to an organ below it directly that is called Gallbladder. The dilation of a bile duct is an important indicator regarding any serious issue in the human body. Number of reasons may cause bile duct dilation, such as: stones, tumors which commonly occur due to pancreas or papilla of vater. In this paper, the main contributions are: 1) a novel framework that consists of three phases to be applied on a set of Magnetic Resonance Imaging (MRI) images 2) an extracted set of features with their accurate values that express the condition of the biliary trees from the MRI images. Such dataset can be used in several applications to determine whether a bile duct is dilated or not. The dataset is organized as the following: half of the MRI images are for normal bile ducts, while the other half is for dilated bile ducts. To extract the useful features to diagnose the medical condition of the bile ducts from the MRI images, we implemented and applied the proposed framework that is started by using the enhanced active contour technique without edges in combination with Denoising Convolutional Neural Networks (DnCNN) to perform the segmentation and features extraction process. After that, the output of the segmentation process is the segmented biliary tree that will be used later to extract the needful features to make a diagnostic decision whether there is a dilation or not by comparing the features values of the normal versus the dilated bile ducts. We applied the feed forward neural network with backpropagation training algorithm for classification purposes. According to the experiments, the overall accuracy of the proposed framework was 90.00%. Such approach improves and increases the accuracy of the physicians’ diagnostic decisions which is considered as of significant importance for treatment and cure.

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A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images

Cited by 20 publications

References 50 publications

Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy

Clinical Implementation of In-House Developed MR-Based Patient-Specific 3D Models of Liver Anatomy

CBCT‐based navigation system for open liver surgery: Accurate guidance toward mobile and deformable targets with a semi‐rigid organ approximation and electromagnetic tracking of the liver

Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

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