Probabilistic liver atlas construction

Durá, Estrella; Domingo, Juan Carlos Ureta; Ayala, Guillermo; Martí‐Bonmatí, Luis; Göçeri, Evgin

doi:10.1186/s12938-016-0305-8

Cited by 10 publications

(3 citation statements)

References 39 publications

(30 reference statements)

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“…Image segmentation refers to the partitioning of a digital image into multiple segments that are sets of pixels (or voxels) which usually represent discrete structures. Approaches to image segmentation prior to ML included probabilistic atlases [ 12 , 13 ], statistical shape models (SSMs) [ 14 , 15 ], graph-cut (GC) algorithms [ 16 , 17 ], and multi-atlas segmentation (MAS) [ 18 ]. Feature extraction is a dimensionality reduction technique used to efficiently represent parts of an image as a compact feature vector.…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Peña-Solórzano

Albrecht

Bassed

et al. 2020

Forensic Science International

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Highlights Machine learning techniques are currently not widely used in addressing forensic imaging problems. Machine learning applications can be transferred from clinical to forensic settings. Convolutional neural networks are outperforming other machine learning techniques in image processing tasks. There is a dearth of quantitative metrics presented for inter-study comparisons. A wider use of histopathological gold standard metrics for assessing algorithm performance is required.

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Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Peña-Solórzano

Albrecht

Bassed

et al. 2020

Forensic Science International

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“…Lamecker et al [11] present for the first time a statistical shape model of the liver. Dura et al [3] present the construction of a a probabilistic liver atlas. In terms of using shape models for the diagnosis of liver related diseases, Kohara et al [10] use a statistical shape model of the liver to assess differences between healthy subjects and individuals diagnosed with cirrhosis.…”

Section: Related Workmentioning

confidence: 99%

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

Gutiérrez-Becker¹,

Gatidis

Gutmann

et al. 2018

Shape in Medical Imaging

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Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.

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“…In the literature, there are also many semi-/full-automated liver segmentation techniques [10][11][12][13][14], vessel segmentation [15,16], labeling of hepatic and portal vessels [17] based on MR/CT images for preevaluation of liver transplantation. These methods present quantitative information to doctors about liver (e.g., liver volume or size) and vascular structures to reduce complications after liver transplantation.…”

Section: Introductionmentioning

confidence: 99%

Epidemiologic Study on the Incidence of Biliary Complications after Liver Transplantation

Silva¹,

Andrade²,

Lisboa³

et al. 2018

J Liver

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Introduction: Biliary complications after hepatic transplantation have a variable incidence of 10 to 25% among all complications after hepatic transplantation and mortality reported up to 10%, have an important impact on the patient's quality of life, since it implies the need for hospitalizations and multiple interventions for treatment. They are more frequent in the first year after transplantation, with progressive reduction of the frequency after 1 year. Objective: To evaluate the risk factors of greater impact in the development of bile duct stenosis in patients after liver transplantation. Methods: One hundred and eighty-eight charts of liver transplant patients were retrospectively evaluated. Inclusion criteria: liver transplanted patients from 2011 to December 2017. Exclusion criteria: deaths that occurred between the first month after transplantation, incomplete medical records and most cases of retransplants, except for patients who retransplanted as a consequence of their own complication. Results: Biliary complications were present in 14% (N=26) of the patients. Of these, 52% (N=21) presented stenosis of the biliary tract, followed by other complications such as cholangitis (20%), fistula calculation (10%), bilioma (3%). Among patients with bile duct stenosis, 19% (N=4) presented non-anastomotic stenosis and 81% (N=17) anastomotic stenosis. The cause of hepatopathy was in 42% of patients (N=9) ethanolic, followed by other causes such as: viral hepatitis, cryptogenic, autoimmune, fulminant hepatitis, NASH, CEP, glycogenosis and 1 case of secondary retransplantation to the bile complication itself. Conclusion: The incidence of biliary complications is comparable with the incidence reported in other institutions. The low incidence of bile duct stenosis reduces the power of the study to identify the most impacting risk factors.

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Probabilistic liver atlas construction

Cited by 10 publications

References 39 publications

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Deep Shape Analysis on Abdominal Organs for Diabetes Prediction

Epidemiologic Study on the Incidence of Biliary Complications after Liver Transplantation

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