Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

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 in MRI: a fully automatic method based on stochastic partitions

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 +/- 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work has been supported by the MITYC under the project NaRALap (ref. TSI-020100-2009-189), partially by the CDTI under the project ONCOTIC (IDI-20101153), by Ministerio de Educacion y Ciencia Spain, Project Game Teen (TIN2010-20187) projects Consolider-C (SEJ2006-14301/PSIC), "CIBER of Physiopathology of Obesity and Nutrition, an initiative of ISCIII" and Excellence Research Program PROMETEO (Generalitat Valenciana. Conselleria de Educacion, 2008-157). We would like to express our gratitude to the Hospital Clinica Benidorm, for providing the MR datasets and to the radiologist team of Inscanner for the manual segmentation of the MR images.López-Mir, F.; Naranjo Ornedo, V.; Angulo, J.; Alcañiz Raya, ML.; Luna, L. (2014). Liver segmentation in MRI: a fully automatic method based on stochastic partitions. Computer Methods and Programs in Biomedicine. 114(1):11-28. https://doi.org/10.1016/j.cmpb.2013.12.022S1128114

AI-basierte volumetrische Analyse der Lebermetastasenlast bei Patienten mit neuroendokrinen Neoplasmen (NEN)

Background: Quantification of liver tumor load in patients with liver metastases from neuroendocrine neoplasms is essential for therapeutic management. However, accurate measurement of three-dimensional (3D) volumes is time-consuming and difficult to achieve. Even though the common criteria for assessing treatment response have simplified the measurement of liver metastases, the workload of following up patients with neuroendocrine liver metastases (NELMs) remains heavy for radiologists due to their increased morbidity and prolonged survival. Among the many imaging methods, gadoxetic acid (Gd-EOB)-enhanced magnetic resonance imaging (MRI) has shown the highest accuracy. Methods: 3D-volumetric segmentation of NELM and livers were manually performed in 278 Gd-EOB MRI scans from 118 patients. Eighty percent (222 scans) of them were randomly divided into training datasets and the other 20% (56 scans) were internal validation datasets. An additional 33 patients from a different time period, who underwent Gd-EOB MRI at both baseline and 12-month follow-up examinations, were collected for external and clinical validation (n = 66). Model measurement results (NELM volume; hepatic tumor load (HTL)) and the respective absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) for baseline and follow-up-imaging were used and correlated with multidisciplinary cancer conferences (MCC) decisions (treatment success/failure). Three readers manually segmented MRI images of each slice, blinded to clinical data and independently. All images were reviewed by another senior radiologist. Results: The model’s performance showed high accuracy between NELM and liver in both internal and external validation (Matthew’s correlation coefficient (ϕ): 0.76/0.95, 0.80/0.96, respectively). And in internal validation dataset, the group with higher NELM volume (> 16.17 cm3) showed higher ϕ than the group with lower NELM volume (ϕ = 0.80 vs. 0.71; p = 0.0025). In the external validation dataset, all response variables (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) reflected significant differences across MCC decision groups (all p < 0.001). The AI model correctly detected the response trend based on ∆relNELM and ∆relHTL in all the 33 MCC patients and showed the optimal discrimination between treatment success and failure at +56.88% and +57.73%, respectively (AUC: 1.000; P < 0.001). Conclusions: The created AI-based segmentation model performed well in the three-dimensional quantification of NELMs and HTL in Gd-EOB-MRI. Moreover, the model showed good agreement with the evaluation of treatment response of the MCC’s decision.Hintergrund: Die Quantifizierung der Lebertumorlast bei Patienten mit Lebermetastasen von neuroendokrinen Neoplasien ist für die Behandlung unerlässlich. Eine genaue Messung des dreidimensionalen (3D) Volumens ist jedoch zeitaufwändig und schwer zu erreichen. Obwohl standardisierte Kriterien für die Beurteilung des Ansprechens auf die Behandlung die Messung von Lebermetastasen vereinfacht haben, bleibt die Arbeitsbelastung für Radiologen bei der Nachbeobachtung von Patienten mit neuroendokrinen Lebermetastasen (NELMs) aufgrund der höheren Fallzahlen durch erhöhte Morbidität und verlängerter Überlebenszeit hoch. Unter den zahlreichen bildgebenden Verfahren hat die Gadoxetsäure (Gd-EOB)-verstärkte Magnetresonanztomographie (MRT) die höchste Genauigkeit gezeigt. Methoden: Manuelle 3D-Segmentierungen von NELM und Lebern wurden in 278 Gd-EOB-MRT-Scans von 118 Patienten durchgeführt. 80% (222 Scans) davon wurden nach dem Zufallsprinzip in den Trainingsdatensatz eingeteilt, die übrigen 20% (56 Scans) waren interne Validierungsdatensätze. Zur externen und klinischen Validierung (n = 66) wurden weitere 33 Patienten aus einer späteren Zeitspanne des Multidisziplinäre Krebskonferenzen (MCC) erfasst, welche sich sowohl bei der Erstuntersuchung als auch bei der Nachuntersuchung nach 12 Monaten einer Gd-EOB-MRT unterzogen hatten. Die Messergebnisse des Modells (NELM-Volumen; hepatische Tumorlast (HTL)) mit den entsprechenden absoluten (ΔabsNELM; ΔabsHTL) und relativen Veränderungen (ΔrelNELM; ΔrelHTL) bei der Erstuntersuchung und der Nachuntersuchung wurden zum Vergleich mit MCC-Entscheidungen (Behandlungserfolg/-versagen) herangezogen. Drei Leser segmentierten die MRT-Bilder jeder Schicht manuell, geblindet und unabhängig. Alle Bilder wurden von einem weiteren Radiologen überprüft. Ergebnisse: Die Leistung des Modells zeigte sowohl bei der internen als auch bei der externen Validierung eine hohe Genauigkeit zwischen NELM und Leber (Matthew's Korrelationskoeffizient (ϕ): 0,76/0,95 bzw. 0,80/0,96). Und im internen Validierungsdatensatz zeigte die Gruppe mit höherem NELM-Volumen (> 16,17 cm3) einen höheren ϕ als die Gruppe mit geringerem NELM-Volumen (ϕ = 0,80 vs. 0,71; p = 0,0025). Im externen Validierungsdatensatz wiesen alle Antwortvariablen (∆absNELM; ∆absHTL; ∆relNELM; ∆relHTL) signifikante Unterschiede zwischen den MCC-Entscheidungsgruppen auf (alle p < 0,001). Das KI-Modell erkannte das Therapieansprechen auf der Grundlage von ∆relNELM und ∆relHTL bei allen 33 MCC-Patienten korrekt und zeigte bei +56,88% bzw. +57,73% eine optimale Unterscheidung zwischen Behandlungserfolg und -versagen (AUC: 1,000; P < 0,001). Schlussfolgerungen: Das Modell zeigte eine hohe Genauigkeit bei der dreidimensionalen Quantifizierung des NELMs-Volumens und der HTL in der Gd-EOB-MRT. Darüber hinaus zeigte das Modell eine gute Übereinstimmung bei der Bewertung des Ansprechens auf die Behandlung mit der Entscheidung des Tumorboards

Enter the matrix:On how to improve thyroid nodule management using 3D ultrasound

Roughly two-thirds of the adult population has a thyroid nodule, of which 90% are benign. Of the adults that have a nodule, approximately 5% will experience symptoms that include a feeling of a marble stuck in the throat, difficulty swallowing and breathing, and cosmetic complaints. Thyroid nodule management primarily makes use of ultrasound as the imaging modality for diagnosis, image guidance during therapy (radiofrequency ablation i.e. RFA), and follow-up. Although ultrasound is relatively easy to apply, it is hard to standardize for repeated measurements and across various users. Further, RFA can benefit from 3D imaging information and a planning and navigation system to improve clinical outcome. These challenges may be overcome by using 3D ultrasound. In this thesis, two phantoms were created on which these methods can be developed. Further, it offers insight into the use of 2D and 3D ultrasound for thyroid nodule management.To assess the impact of changes to an intervention, a baseline was determined of the effectiveness of RFA in Dutch hospitalsUsing a simple phantom, we have shown that utilizing a volume-based measurement technique, that the matrix transducer offers, results in improved measurement accuracy. The more complex, anthropomorphic, phantom serves as a platform on which thermal treatments, such as RFA, can be improved. Using this phantom, we have shown that the impact of 2D and 3D ultrasound on RFA efficacy does not differ from one another; however, the matrix transducer might be more user-friendly for needle placement due to the dual-plane imaging. An additional use case for these phantoms is their capacity to compare dominant and non-dominant hand ablations, as well as serve as a training platform. Additional research is required that employs more operators to find stronger evidence supporting a difference between the ablating hands and the difference in effect of 2D and 3D ultrasound guidance.To make full use of 3D ultrasound, stitching algorithms should be integrated into the ultrasound systems to acquire larger volumes. These can then be processed by deep-learning algorithms for use in computer-aided diagnosis and intervention systems. To further improve the applicability of 3D ultrasound in the clinic, integrating analysis methods such as 3D elastography and 3D Doppler is suggested

Coupled Shape Models for the Diagnosis of Organ Motion Restriction

Annähernd 30% der weltweiten Todesfälle sind auf Erkrankungen des Herzens und der Lunge zurückzuführen, wobei die meisten dieser Erkrankungen während ihres Verlaufs die Mobilität des betroffenen Organs verändern. Viele dieser To-desfälle könnten durch eine frühzeitige Erkennung und Behandlung der Erkran-kung vermieden werden. Deshalb wurden im Zuge dieser Arbeit Methoden ent-wickelt, um aus Segmentierungen von dynamischen Magnetresonanztomogra-phie-Daten quantitative Kennzahlen für die funktionale Analyse der Herz- und Lungenbewegung zu generieren. Ein automatisiertes Segmentierungsverfahren basierend auf gekoppelten Formmodellen wurde entwickelt, welches wechsel-seitige Informationen der Form und Geometrie mehrerer korrelierter Objekte mit einbezieht, und somit 40% bessere Ergebnisse im Vergleich zur Verwendung einzelner Modelle erzielte. Im Fall des Herzens wurde ein Volumenberechnungs-fehler von unter 13% erreicht, was in der Größenordnung der Interobserver-Variabilität liegt. Für die Lunge konnte ein Volumenfehler von unter 70ml gezeigt werden. Aus den Segmentierungsergebnissen wurden funktionale Parameter der lokalen Organdynamik abgeleitet und visualisiert, die gegen konventionelle Diag-nosemethoden evaluiert wurden und dabei gute Übereinstimmung zeigen, dar-über hinaus jedoch eine lokal und regionale Mobilitätscharakterisierung erlau-ben

Entwicklungen und Untersuchungen zur Bildgebung der Schilddrüse: 124Iod-PET/CT, 3D-Ultraschall und nuklearmedizinisch-sonographische Bildfusion

In der etablierten Schilddrüsenbildgebung existieren trotz des bereits hohen Standards begrenzende Faktoren. Methodische und technische Neuerungen erscheinen mithin sinnvoll und geboten. Die vorliegende Habilitationsschrift stellt die Entwicklung und Erprobung neuer Konzepte der Schilddrüsendiagnostik in drei Teilgebieten vor: *Durch die 124Iod-Niedrigaktivitäts-PET/Niedrigdosis-CT wird (i) die Ortsauflösung der herkömmlichen Szintigraphie übertroffen und die Detektierbarkeit kleinerer Strukturen sowie anatomischer Details verbessert. Durch den parallel akquirierten CT-Datensatz können (ii) zusätzliche Erkenntnisse zur Schilddrüse sowie deren Beziehung zu Nachbarorganen gewonnen werden. Darüber hinaus sind (iii) im Rahmen der Vorbereitung von Radiojodtherapien prätherapeutische Uptake-Messungen möglich. *Der 3D-US ermöglicht (i) den lückenlosen Scan der Schilddrüse und (ii) die vollständige digitale Archivierung des Untersuchungsvolumens im PACS. Dadurch ergeben sich auf Schnittbildworkstations die Vorteile (iii) des Second Readings, (iv) des Side-by-Side-Vergleichs mit vorangegangenen 3D-US-Studien und anderen Schnittbildverfahren. Darüber hinaus kann (v) eine nachträgliche Datenverarbeitung (Processing) erfolgen. *Die Einbeziehung des Ultraschalls in das Konzept der Fusions- bzw. Hybridbildgebung hat gezeigt, dass die räumliche Verknüpfung und bildliche Überlagerung der morphologisch-sonographischen Informationen mit den nuklearmedizinisch-funktionellen Bilddaten erfolgen kann. Aus dem klinischen Potential der Methoden einerseits, sowie den geschilderten Limitationen andererseits ergeben sich Implikationen für die Zukunft. Zunächst sind die apparativ-technische Weiterentwicklung der Verfahren sowie eine Optimierung der informationstechnischen Einbindung notwendig. Darüber hinaus muss eine Entwicklung hin zu einer zeitsparenden und einfachen Anwendbarkeit erfolgen, um einen rationellen klinischen Workflow zu ermöglichen und personelle Ressourcen zu schonen

수치 모델과 그래프 이론을 이용한 향상된 영상 분할 연구 -폐 영상에 응용-

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2016. 2. 김희찬.This dissertation presents a thoracic cavity segmentation algorithm and a method of pulmonary artery and vein decomposition from volumetric chest CT, and evaluates their performances. The main contribution of this research is to develop an automated algorithm for segmentation of the clinically meaningful organ. Although there are several methods to improve the organ segmentation accuracy such as the morphological method based on threshold algorithm or the object selection method based on the connectivity information our novel algorithm uses numerical algorithms and graph theory which came from the computer engineering field. This dissertation presents a new method through the following two examples and evaluates the results of the method. The first study aimed at the thoracic cavity segmentation. The thoracic cavity is the organ enclosed by the thoracic wall and the diaphragm surface. The thoracic wall has no clear boundary. Moreover since the diaphragm is the thin surface, this organ might have lost parts of its surface in the chest CT. As the previous researches, a method which found the mediastinum on the 2D axial view was reported, and a thoracic wall extraction method and several diaphragm segmentation methods were also informed independently. But the thoracic cavity volume segmentation method was proposed in this thesis for the first time. In terms of thoracic cavity volumetry, the mean±SD volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), and false negative ratio on VOR (FNRV) of the proposed method were 98.17±0.84%, 0.49±0.23%, and 1.34±0.83%, respectively. The proposed semi-automatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful for clinical purposes. The second study proposed a method to decompose the pulmonary vessel into vessel subtrees for separation of the artery and vein. The volume images of the separated artery and vein could be used for a simulation support data in the lung cancer. Although a clinician could perform the separation in his imagination, and separate the vessel into the artery and vein in the manual, an automatic separation method is the better method than other methods. In the previous semi-automatic method, root marking of 30 to 40 points was needed while tracing vessels under 2D slice view, and this procedure needed approximately an hour and a half. After optimization of the feature value set, the accuracy of the arterial and venous decomposition was 89.71 ± 3.76% in comparison with the gold standard. This framework could be clinically useful for studies on the effects of the pulmonary arteries and veins on lung diseases.Chapter 1 General Introduction 2 1.1 Image Informatics using Open Source 3 1.2 History of the segmentation algorithm 5 1.3 Goal of Thesis Work 8 Chapter 2 Thoracic cavity segmentation algorithm using multi-organ extraction and surface fitting in volumetric CT 10 2.1 Introduction 11 2.2 Related Studies 13 2.3 The Proposed Thoracic Cavity Segmentation Method 16 2.4 Experimental Results 35 2.5 Discussion 41 2.6 Conclusion 45 Chapter 3 Semi-automatic decomposition method of pulmonary artery and vein using two level minimum spanning tree constructions for non-enhanced volumetric CT 46 3.1 Introduction 47 3.2 Related Studies 51 3.3 Artery and Vein Decomposition 55 3.4 An Efficient Decomposition Method 70 3.5 Evaluation 75 3.6 Discussion and Conclusion 85 References 88 Abstract in Korean 95Docto

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice