1,262 research outputs found
An Automated Liver Vasculature Segmentation from CT Scans for Hepatic Surgical Planning
Liver vasculature segmentation is a crucial step for liver surgical planning. Segmentation of liver vasculature is an important part of the 3D visualisation of the liver anatomy. The spatial relationship between vessels and other liver structures, like tumors and liver anatomic segments, helps in reducing the surgical treatment risks. However, liver vessels segmentation is a challenging task, that is due to low contrast with neighboring parenchyma, the complex anatomy, the very thin branches and very small vessels. This paper introduces a fully automated framework consist of four steps to segment the vessels inside the liver organ. Firstly, in the preprocessing step, a combination of two filtering techniques are used to extract and enhance vessels inside the liver region, first the vesselness filter is used to extract the vessels structure, and then the anisotropic coherence enhancing diffusion (CED) filter is used to enhance the intensity within the tubular vessels structure. This step is followed by a smart multiple thresholding to extract the initial vasculature segmentation. The liver vasculature structures, including hepatic veins connected to the inferior vena cava and the portal veins, are extracted. Finally, the inferior vena cava is segmented and excluded from the vessels segmentation, as it is not considered as part of the liver vasculature structure. The liver vessel segmentation method is validated on the publically available 3DIRCAD datasets. Dice coefficient (DSC) is used to evaluate the method, the average DSC score achieved a score 68.5%. The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning
Semiautomated 3D liver segmentation using computed tomography and magnetic resonance imaging
Le foie est un organe vital ayant une capacitĂ© de rĂ©gĂ©nĂ©ration exceptionnelle et un rĂŽle crucial dans le fonctionnement de lâorganisme. LâĂ©valuation du volume du foie est un outil important pouvant ĂȘtre utilisĂ© comme marqueur biologique de sĂ©vĂ©ritĂ© de maladies hĂ©patiques. La volumĂ©trie du foie est indiquĂ©e avant les hĂ©patectomies majeures, lâembolisation de la veine porte et la transplantation.
La méthode la plus répandue sur la base d'examens de tomodensitométrie (TDM) et d'imagerie par résonance magnétique (IRM) consiste à délimiter le contour du foie sur plusieurs coupes consécutives, un processus appelé la «segmentation».
Nous prĂ©sentons la conception et la stratĂ©gie de validation pour une mĂ©thode de segmentation semi-automatisĂ©e dĂ©veloppĂ©e Ă notre institution. Notre mĂ©thode reprĂ©sente une approche basĂ©e sur un modĂšle utilisant lâinterpolation variationnelle de forme ainsi que lâoptimisation de maillages de Laplace. La mĂ©thode a Ă©tĂ© conçue afin dâĂȘtre compatible avec la TDM ainsi que l' IRM.
Nous avons Ă©valuĂ© la rĂ©pĂ©tabilitĂ©, la fiabilitĂ© ainsi que lâefficacitĂ© de notre mĂ©thode semi-automatisĂ©e de segmentation avec deux Ă©tudes transversales conçues rĂ©trospectivement. Les rĂ©sultats de nos Ă©tudes de validation suggĂšrent que la mĂ©thode de segmentation confĂšre une fiabilitĂ© et rĂ©pĂ©tabilitĂ© comparables Ă la segmentation manuelle. De plus, cette mĂ©thode diminue de façon significative le temps dâinteraction, la rendant ainsi adaptĂ©e Ă la pratique clinique courante.
Dâautres Ă©tudes pourraient incorporer la volumĂ©trie afin de dĂ©terminer des marqueurs biologiques de maladie hĂ©patique basĂ©s sur le volume tels que la prĂ©sence de stĂ©atose, de fer, ou encore la mesure de fibrose par unitĂ© de volume.The liver is a vital abdominal organ known for its remarkable regenerative
capacity and fundamental role in organism viability. Assessment of liver volume is
an important tool which physicians use as a biomarker of disease severity. Liver
volumetry is clinically indicated prior to major hepatectomy, portal vein
embolization and transplantation.
The most popular method to determine liver volume from computed
tomography (CT) and magnetic resonance imaging (MRI) examinations involves
contouring the liver on consecutive imaging slices, a process called
âsegmentationâ. Segmentation can be performed either manually or in an
automated fashion.
We present the design concept and validation strategy for an innovative
semiautomated liver segmentation method developed at our institution. Our
method represents a model-based approach using variational shape interpolation
and Laplacian mesh optimization techniques. It is independent of training data,
requires limited user interactions and is robust to a variety of pathological cases.
Further, it was designed for compatibility with both CT and MRI examinations.
We evaluated the repeatability, agreement and efficiency of our
semiautomated method in two retrospective cross-sectional studies. The results of
our validation studies suggest that semiautomated liver segmentation can provide
strong agreement and repeatability when compared to manual segmentation.
Further, segmentation automation significantly shortens interaction time, thus
making it suitable for daily clinical practice.
Future studies may incorporate liver volumetry to determine volume-averaged
biomarkers of liver disease, such as such as fat, iron or fibrosis measurements per
unit volume. Segmental volumetry could also be assessed based on
subsegmentation of vascular anatomy
Liver Segmentation and its Application to Hepatic Interventions
The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning
Deep learning for image-based liver analysis â A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
Hepatic Vessel Segmentation for 3D Planning of Liver Surgery
none8Rationale and Objectives: The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic
segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver
surgery.
Materials and Methods: A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to
a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic
data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy
was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the
automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.
Results: The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the
range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and
manually segmented vessel sections, with an average difference of 0.06 mm2. The average values of corresponding false-positive and
false-negative ratios were 7.7% and 2.3%, respectively.
Conclusions: A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed
tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel
delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative
resections.Francesco Conversano;Roberto Franchini;Christian Demitri;Laurent Massoptier;Francesco Montagna;Alfonso Maffezzoli;Antonio Malvasi;Sergio CasciaroFrancesco, Conversano; Roberto, Franchini; Demitri, Christian; Laurent, Massoptier; Francesco, Montagna; Maffezzoli, Alfonso; Antonio, Malvasi; Sergio, Casciar
Automatic liver vessel segmentation using 3D region growing and hybrid active contour model
This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56âŻmm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms
Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations
There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, showcasing the
different machine learning algorithms researchers have used to segment the
liver, hepatic-tumors, and hepatic-vasculature structures. We divide the
surveyed studies based on the tissue of interest (hepatic-parenchyma,
hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more
than one task simultaneously. Additionally, the machine learning algorithms are
classified as either supervised or unsupervised, and further partitioned if the
amount of works that fall under a certain scheme is significant. Moreover,
different datasets and challenges found in literature and websites, containing
masks of the aforementioned tissues, are thoroughly discussed, highlighting the
organizers original contributions, and those of other researchers. Also, the
metrics that are used excessively in literature are mentioned in our review
stressing their relevancy to the task at hand. Finally, critical challenges and
future directions are emphasized for innovative researchers to tackle, exposing
gaps that need addressing such as the scarcity of many studies on the vessels
segmentation challenge, and why their absence needs to be dealt with in an
accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver
tissues segmentation based on automated ML-based technique
Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery
International audienceOBJECTIVE: To improve the planning of hepatic surgery, we have developed a fully automatic anatomical, pathological, and functional segmentation of the liver derived from a spiral CT scan. MATERIALS AND METHODS: From a 2 mm-thick enhanced spiral CT scan, the first stage automatically delineates skin, bones, lungs, kidneys, and spleen by combining the use of thresholding, mathematical morphology, and distance maps. Next, a reference 3D model is immersed in the image and automatically deformed to the liver contours. Then an automatic Gaussian fitting on the imaging histogram estimates the intensities of parenchyma, vessels, and lesions. This first result is next improved through an original topological and geometrical analysis, providing an automatic delineation of lesions and veins. Finally, a topological and geometrical analysis based on medical knowledge provides hepatic functional information that is invisible in medical imaging: portal vein labeling and hepatic anatomical segmentation according to the Couinaud classification. RESULTS: Clinical validation performed on more than 30 patients shows that delineation of anatomical structures by this method is often more sensitive and more specific than manual delineation by a radiologist. CONCLUSION: This study describes the methodology used to create the automatic segmentation of the liver with delineation of important anatomical, pathological, and functional structures from a routine CT scan. Using the methods proposed in this study, we have confirmed the accuracy and utility of the creation of a 3D liver model compared with the conventional reading of the CT scan by a radiologist. This work may allow improved preoperative planning of hepatic surgery by more precisely delineating liver pathology and its relationship to normal hepatic structures. In the future, this data may be integrated with computer-assisted surgery and thus represents a first step towards the development of an augmented-reality surgical system
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