Semiautomated 3D liver segmentation using computed tomography and magnetic resonance imaging

Le foie est un organe vital ayant une capacité de régénération exceptionnelle et un rôle crucial dans le fonctionnement de l’organisme. L’évaluation du volume du foie est un outil important pouvant être utilisé comme marqueur biologique de sévérité de maladies hépatiques. La volumétrie du foie est indiquée avant les hépatectomies majeures, l’embolisation 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 présentons la conception et la stratégie de validation pour une méthode de segmentation semi-automatisée développée à notre institution. Notre méthode représente une approche basée sur un modèle utilisant l’interpolation variationnelle de forme ainsi que l’optimisation de maillages de Laplace. La méthode a été conçue afin d’être compatible avec la TDM ainsi que l' IRM. Nous avons évalué la répétabilité, la fiabilité ainsi que l’efficacité de notre méthode semi-automatisée de segmentation avec deux études transversales conçues rétrospectivement. Les résultats de nos études de validation suggèrent que la méthode de segmentation confère une fiabilité et répétabilité comparables à la segmentation manuelle. De plus, cette méthode diminue de façon significative le temps d’interaction, la rendant ainsi adaptée à la pratique clinique courante. D’autres études pourraient incorporer la volumétrie afin de déterminer des marqueurs biologiques de maladie hépatique basés sur le volume tels que la présence de stéatose, de fer, ou encore la mesure de fibrose par unité 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 “segmentation”. 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 for Hepatic Lesions Detection and Characterisation

The detection and characterisation of hepatic lesions is fundamental in clinical practice, from the diagnosis stages to the evolution of the therapeutic response. Hepatic magnetic resonance is a usual practice in the localization and quantification of lesions. Automatic segmentation of the liver is illustrated in T1 weighted images. This task is necessary for detecting the lesions. The proposed liver segmentation is based on 3D anisotropic diffusion processing without any control parameter. Combinations of edge detection techniques, histogram analysis, morphological post-processing and evolution of an active contour have been applied to the liver segmentation. The active contour evolution is based on the minimization of variances in luminance between the liver and its closest neighbourhood

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

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Relationship between primary liver hepatocellular carcinoma volumes on portal-venous phase CT imaging

The liver is an important organ in the body. It is located under the rib cage on the right side. The liver performs many important functions, it processes food for nutrients that the body requires and also helps in the detoxification of harmful materials. Like any organ in the body, the liver is susceptible to diseases such as liver cancer. Liver cancer is the growth and spread of unhealthy cells of the liver. There are several risk factor for liver cancer, these are: Cirrhosis (scarring of the liver), long term hepatitis B and hepatitis C infection and diabetes patients with long term drinking problem. Hepatocellular Carcinoma is the most common form of liver cancer in adult population which begins in the main type of liver cell (hepatocyte). Because Hepatocellular carcinoma starts from the primary liver cell itself (hepatocytes), as such it is a primary liver cancer. About 30,000 Americans are diagnosed with primary liver cancer yearly, making it an important disease that plaques our society and therefore needs proper diagnosis. In clinical evaluation of primary liver cancer such as HCC, the use of medical imaging technology has been commonplace. Most medical facilities across the country and globally typically use Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) in the diagnosis and treatment follow up of Hepatocellular carcinoma. The medical imaging devices are used to determine the extent and volume of the tumor of the cancerous liver cells. In clinical trials involving the imaging of HCC tumors, the typical protocol used in the CT imaging of HCC involves the use of contrast enhanced dual phase acquisition. This approach is based on the physiology of the blood flow through the liver. Since HCC tumors are hypervascular in nature, it would thus be more apparent in the arterial phase of an acquired CT image. The aforementioned characteristic was tested with a volume paradigm which measure and compare the volume of both the arterial phase and portal venous phase acquired images in the experiment. Overall this study helps in furthering goals to reduce the patient dose from the x-ray tubes during clinical trials. The results of the experiments (n = 19, t = 0.67, p = 0.26), indicates no significant difference between the volume of the HCC tumor images acquired both in the AP and PVP

Segmentación de Lesiones Hepáticas Adquiridas por Resonancia Magnética.

La detección y caracterización de lesiones hepáticas resulta fundamental en la práctica clínica, desde las etapas de diagnosis hasta la evolución de la respuesta terapéutica. La resonancia magnética hepática es una práctica habitual en la localización y cuantificación de las lesiones. Se presenta la segmentación automática de lesiones hepáticas en imágenes potenciadas en T1. La segmentación propuesta se basa en un procesado de difusión anisotrópica 3D adaptativo y carente de parámetros de control. A la imagen realzada se le aplica una combinación de técnicas de detección de bordes 3D, análisis del histograma, post procesado morfológico y evolución de un contorno activo 3D. Éste último fusiona información de apariencia y forma de la lesión