Úvod: Umělé neuronové sítě se stávají důležitou technologií při analýze dat a jejich vliv začíná prostupovat i do oblasti medicíny. Naše pracoviště se dlouhodobě věnuje experimentální chirurgii, na to navazuje náš zájem o pokrok v ostatních oblastech moderních technologií a tím i umělých neuronových sítí. V rámci aktuálního čísla chceme prozkoumat i tento aspekt technického pokroku. Hlavním cílem je kritické zhodnocení silných i slabých stránek technologie umělých neuronových sítí s ohledem na využití v klinické a experimentální chirurgii. Metody: V článku je věnována pozornost in-silico modelování a zejména pak možnostem neuronových sítí s ohledem na zpracování obrazových dat v medicíně. V textu je krátce shrnut historický vývoj hlubokého učení neuronových sítí a základní principy jejich fungování. Dále je představena taxonomie základních řešených úloh. Zmíněny jsou i možné problémy při učení i s možnostmi jejich řešení. Výsledky: Článek poukazuje na rozličné možnosti umělých neuronových sítí v biologických aplikacích. Na řadě biomedicínských aplikací umělých neuronových sítí popisuje rozdělení a princip základních úloh strojového učení a hlubokého učení – klasifikace, detekce a segmentace. Závěr: Aplikace metod umělých neuronových sítí mají v medicíně a chirurgii značný potenciál. Obcházejí potřebu zdlouhavého subjektivního nastavování parametrů znalostním inženýrem, neboť se učí přímo z dat. Při využití nevhodně vyváženého datasetu však může docházet k neočekávaným, avšak zpětně vysvětlitelným chybám. Řešení představuje vytvoření dostatečně bohatého datasetu pro učení a ověření funkce.

Introduction: Artificial neural networks are becoming an essential technology in data analysis, and their influence is starting to permeate the field of medicine. Experimental surgery has been a long-term subject of study of our lab; this is naturally reflected in our interest in other areas of modern technologies including artificial neural networks and their advancements. In the current issue, we would like to explore this aspect of technical progress. The main goal is to critically evaluate the strengths and weaknesses of artificial neural network technology concerning its use in clinical and experimental surgery. Methods: The article is focused on in-silico modeling, particularly on the potential of neural networks in terms of image data processing in medicine. The text briefly summarizes the historical development of deep learning neural networks and their basic principles. Furthermore, basic taxonomy tasks are presented. Finally, potential learning problems and possible solutions are also mentioned. Results: The article points out various possible uses of artificial neural networks in biological applications. Several biomedical applications of artificial neural networks are used to describe the division and principles of the most common tasks of machine learning and deep learning such as classification, detection, and segmentation. Conclusion: The application of artificial neural network methods in medicine and surgery offers a considerable potential; by learning directly from the data, they make it possible to avoid lengthy and subjective setting of parameters by an expert engineer. Nevertheless, the use of an unbalanced dataset can lead to unexpected, although traceable errors. The solution is to collect a dataset large enough to enable both learning and verification of proper functionality.

• MeSH
• Deep Learning MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Image Processing, Computer-Assisted MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH

Liver volumetry is an important tool in clinical practice. The calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git.

• Publication type
• Journal Article MeSH

Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy.

• MeSH
• Liver * diagnostic imaging   MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted * MeSH
• Semantics * MeSH
• Publication type
• Letter MeSH

Decellularized scaffolds can serve as an excellent three-dimensional environment for cell repopulation. They maintain tissue-specific microarchitecture of extracellular matrix proteins with important spatial cues for cell adhesion, migration, growth, and differentiation. However, criteria for quality assessment of the three-dimensional structure of decellularized scaffolds are rather fragmented, usually study-specific, and mostly semi-quantitative. Thus, we aimed to develop a robust structural assessment system for decellularized porcine liver scaffolds. Five scaffolds of different quality were used to establish the new evaluation system. We combined conventional semi-quantitative scoring criteria with a quantitative scaffold evaluation based on automated image analysis. For the quantitation, we developed a specific open source software tool (ScaffAn) applying algorithms designed for texture analysis, segmentation, and skeletonization. ScaffAn calculates selected parameters characterizing structural features of porcine liver scaffolds such as the sinusoidal network. After evaluating individual scaffolds, the total scores predicted scaffold interaction with cells in terms of cell adhesion. Higher scores corresponded to higher numbers of cells attached to the scaffolds. Moreover, our analysis revealed that the conventional system could not identify fine differences between good quality scaffolds while the additional use of ScaffAn allowed discrimination. This led us to the conclusion that only using the combined score resulted in the best discrimination between different quality scaffolds. Overall, our newly defined evaluation system has the potential to select the liver scaffolds most suitable for recellularization, and can represent a step toward better success in liver tissue engineering.

• Publication type
• Journal Article MeSH

Quantification of the structure and composition of biomaterials using micro-CT requires image segmentation due to the low contrast and overlapping radioopacity of biological materials. The amount of bias introduced by segmentation procedures is generally unknown. We aim to develop software that generates three-dimensional models of fibrous and porous structures with known volumes, surfaces, lengths, and object counts in fibrous materials and to provide a software tool that calibrates quantitative micro-CT assessments. Virtual image stacks were generated using the newly developed software TeIGen, enabling the simulation of micro-CT scans of unconnected tubes, connected tubes, and porosities. A realistic noise generator was incorporated. Forty image stacks were evaluated using micro-CT, and the error between the true known and estimated data was quantified. Starting with geometric primitives, the error of the numerical estimation of surfaces and volumes was eliminated, thereby enabling the quantification of volumes and surfaces of colliding objects. Analysis of the sensitivity of the thresholding upon parameters of generated testing image sets revealed the effects of decreasing resolution and increasing noise on the accuracy of the micro-CT quantification. The size of the error increased with decreasing resolution when the voxel size exceeded 1/10 of the typical object size, which simulated the effect of the smallest details that could still be reliably quantified. Open-source software for calibrating quantitative micro-CT assessments by producing and saving virtually generated image data sets with known morphometric data was made freely available to researchers involved in morphometry of three-dimensional fibrillar and porous structures in micro-CT scans.

• MeSH
• Biocompatible Materials analysis   MeSH
• Surface Properties MeSH
• X-Ray Microtomography methods   MeSH
• Software MeSH
• Materials Testing * methods   MeSH
• Imaging, Three-Dimensional MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

Úvod: Sinusoidální obstrukční syndrom (SOS) je onemocnění vznikající na základě toxického poškození jaterních sinusoid. Tento syndrom bývá nejčastěji navozen myeloablativní radiochemoterapií u pacientů před transplantací hematopoetických kmenových buněk, dále pak oxaliplatinou především u pacientů s jaterními metastázami kolorektálního karcinomu. Cílem naší studie bylo etablovat model SOS na velkém zvířeti, který by umožnil další studium tohoto onemocnění a usnadnil translaci experimentálních výsledků do humánní medicíny. Metody: Do této pilotní studie bylo zařazeno celkem 27 prasat domácích (plemeno – přeštické černostrakaté prase) (12 samic). Z toho 5 zvířat tvořilo skupinu s vyšším dávkováním monokrotalinu (180 mg/kg) a u zbylých 22 byla podaná dávka monokrotalinu nižší (36 mg/kg). Monocrotalin byl aplikován intraportálně a za týden po jeho aplikaci byla provedena resekce levého laterálního laloku jater. Zvířata byla sledována celkem 3 týdny po aplikaci monokrotalinu. Byla prováděna pravidelná ultrasonografická vyšetření, stanovovány biochemické markery jaterních a ledvinných funkcí a ze získaných bioptických vzorků jaterního parenchymu provedeno histologické vyšetření. Výsledky: Charakter toxického postižení jater, které jsme zaznamenali u všech zvířat, odpovídal jak makroskopicky, tak mikroskopicky obrazu SOS. Zaznamenali jsme elevaci AST, ALT, bilirubinu a amoniaku po aplikaci monokrotalinu. Při ultrasonografickém vyšetření byla patrná vyšší echogenita poškozeného jaterního parenchymu v porovnání s parenchymem zdravým. Ze skupiny prvních pěti zvířat, kterým byla aplikována dávka 180 mg/kg, uhynula všechna zvířata ještě před resekcí levého laterálního laloku jater (1. až 3. den po aplikaci). Ve druhé skupině 22 prasat s nižším dávkováním došlo k úmrtí před provedením jaterní resekce ve 3 případech (6. a 7. den). K předčasnému úmrtí po resekci jater došlo v 8 případech (7. až 17. den po aplikaci). 11 zvířat přežívalo po celou dobu experimentu. Příčinou úmrtí (v rámci obou skupin) byl u 10 zvířat metabolický rozvrat a u 4 zvířat exsanguinace. V obou případech se jednalo o důsledek těžké hepatopatie. 2 ze zvířat zemřela z důvodu nesouvisejícího přímo s intoxikací monokrotalinem (strangulace tenkého střeva, gastrektázie). Závěr: Etablovali jsme model SOS na velkém zvířeti navozený aplikací monokrotalinu o dávce 36 mg/kg cestou portální žíly. Jedná se o první nám známý model SOS navozeného monokrotalinem na velkém zvířeti. Tento model může pomoci při výzkumu jak terapeutického ovlivnění, tak pro hodnocení efektu chirurgické léčby v terénu SOS.

Introduction: Sinusoidal obstruction syndrome (SOS) is a disease which is caused by toxic injury to hepatic sinusoids. This syndrome is most frequently caused by myeloablative radiochemotherapy in patients before hematopoietic stem cells transplantation and also by oxaliplatin mainly in patients with colorectal liver metastases. The aim of this study was to establish a large animal model of SOS, which would enable further study of this disease and facilitate translation of experimental outcomes into human medicine. Methods: A total of 27 domestic pigs (Prestice Black-Pied pig) were involved in this study (12 females). A group with a higher dose of monocrotaline (180 mg/kg) included 5 animals, and the remaining 22 pigs formed another group with a lower dose (36 mg/kg). Monocrotaline was administered via the portal vein and one week after the administration, partial hepatectomy of the left lateral liver lobe was performed. The animals were followed up for 3 weeks after monocrotaline administration. Regular ultrasound examinations were performed as well as examination of biochemical markers of liver and kidney functions and histological examination of liver parenchyma samples. Results: The features of toxic liver injury which we observed in case of all animals were comparable with macroscopic and microscopic appearance of SOS. We recorded AST, ALT, bilirubin and ammonia elevation after monocrotaline administration. Echogenicity on ultrasound images of injured liver parenchyma was higher compared to echogenicity of healthy parenchyma. All the five animals from the first group with a higher monocrotaline dose had died before partial hepatectomy (1st–3rd day after monocrotaline administration). Death before partial hepatectomy occurred in 3 cases (6th and 7th day after monocrotaline administration) in the second group of 22 animals with a lower dose of monocrotaline. Death after partial hepatectomy occurred in 8 cases (7th–17th day after moncrotaline administration) in the same group. 11 animals survived the entire experimental period. The cause of death (in both groups) was metabolic failure in 10 animals and exsanguination in 4 animals, both due to severe hepatopathy. Death of 2 animals was not associated with monocrotaline intoxication (strangulation of small intestine, gastrectasis). Conclusions: We established a large animal model of SOS induced by monocrotaline administration (36 mg/kg via portal vein). This model can contribute to research of therapeutic modalities for this disease or to evaluation of surgical treatment of patients with SOS.

• Keywords
• oxaliplatina, hepatotoxicita,
• MeSH
• Histological Techniques methods   MeSH
• Hepatic Veno-Occlusive Disease * etiology   MeSH
• Chemical and Drug Induced Liver Injury MeSH
• Disease Models, Animal MeSH
• Monocrotaline * adverse effects   MeSH
• Drug-Related Side Effects and Adverse Reactions MeSH
• Swine MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Biomedical Research MeSH
• Surgical Procedures, Operative MeSH
• General Surgery MeSH
• Animal Experimentation MeSH

• Conspectus
• Ortopedie. Chirurgie. Oftalmologie
• NML Fields
• chirurgie
• experimentální medicína
• NML Publication type
• kolektivní monografie

• Keywords
• avermektiny,
• MeSH
• Anthelmintics isolation & purification   MeSH
• Antimalarials isolation & purification   MeSH
• Antiparasitic Agents isolation & purification   MeSH
• Artemisinins chemical synthesis   therapeutic use   MeSH
• Pharmacology history   MeSH
• Ivermectin analogs & derivatives   therapeutic use   MeSH
• Humans MeSH
• Nobel Prize * MeSH
• Drug Discovery * history   MeSH
• Parasitology MeSH
• Check Tag
• Humans MeSH

• MeSH
• Biliary Tract Surgical Procedures methods   MeSH
• Cytokines therapeutic use   MeSH
• Animal Experimentation * MeSH
• Liver surgery   MeSH
• Chemical and Drug Induced Liver Injury surgery   MeSH
• Disease Models, Animal * MeSH
• Antibodies, Monoclonal MeSH
• Parenchymal Tissue surgery   pathology   MeSH
• Pilot Projects MeSH
• Liver Regeneration MeSH
• Sus scrofa surgery   MeSH
• Embolization, Therapeutic methods   MeSH
• Transforming Growth Factor beta1 MeSH
• Portal Vein MeSH
• Animals MeSH
• Check Tag
• Animals MeSH

PURPOSE: Quantitative description of hepatic microvascular bed could contribute to understanding perfusion CT imaging. Micro-CT is a useful method for the visualization and quantification of capillary-passable vascular corrosion casts. Our aim was to develop and validate open-source software for the statistical description of the vascular networks in micro-CT scans. METHODS: Porcine hepatic microvessels were injected with Biodur E20 resin, and the resulting corrosion casts were scanned with 1.9-4.7 [Formula: see text] resolution. The microvascular network was quantified using newly developed QuantAn software both in randomly selected volume probes (n = 10) and in arbitrarily outlined hepatic lobules (n = 4). The volumes, surfaces, lengths, and numbers of microvessel segments were estimated and validated in the same data sets with manual stereological counting. Calculations of tortuosity, radius histograms, length histograms, exports of the skeletonized vascular network into open formats, and an assessment of the degree of their anisotropy were performed. RESULTS: Within hepatic lobules, the microvessels had a volume fraction of 0.13 [Formula: see text] 0.05, surface density of 21.0 [Formula: see text] 2.0 [Formula: see text], length density of 169.0 [Formula: see text] 40.2 [Formula: see text], and numerical density of 588.5 [Formula: see text] 283.1 [Formula: see text]. Sensitivity analysis of the automatic analysis to binary opening, closing, threshold offset, and aggregation radius of branching nodes was performed. CONCLUSION: The software QuantAn and its source code are openly available to researchers working in the field of stochastic geometry of microvessels in micro-CT scans or other three-dimensional imaging methods. The implemented methods comply with reproducible stereological techniques, and they were highly consistent with manual counting. Preliminary morphometrics of the classical hepatic lobules in pig were provided.

• MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Liver blood supply   diagnostic imaging   MeSH
• Corrosion MeSH
• Microvessels diagnostic imaging   MeSH
• Swine MeSH
• X-Ray Microtomography methods   MeSH
• Software MeSH
• Imaging, Three-Dimensional methods   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH