An integrated model-based software for FUS in moving abdominal organs.

Focused ultrasound surgery (FUS) is a non-invasive method for tissue ablation that has the potential for complete and controlled local tumour destruction with minimal side effects. The treatment of abdominal organs such as the liver, however, requires particular technological support in order to enable a safe, efficient and effective treatment. As FUS is applied from outside the patient's body, suitable imaging methods, such as magnetic resonance imaging or diagnostic ultrasound, are needed to guide and track the procedure. To facilitate an efficient FUS procedure in the liver, the organ motion during breathing and the partial occlusion by the rib cage need to be taken into account in real time, demanding a continuous patient-specific adaptation of the treatment configuration. Modelling the patient's respiratory motion and combining this with tracking data improves the accuracy of motion predictions. Modelling and simulation of the FUS effects within the body allows the use of treatment planning and has the potential to be used within therapy to increase knowledge about the patient status. This article describes integrated model-based software for patient-specific modelling and prediction for FUS treatments of moving abdominal organs.

Toward focused ultrasound liver surgery under free breathing.

Focused ultrasound surgery is an outstanding novel technique for cancer treatment because it is completely noninvasive with the potential for complete and controlled local tumor destruction. Because focused ultrasound surgery is applied from outside of the patient's body, imaging such as ultrasound or magnetic resonance imaging is required to plan and monitor the intervention. For the treatment of liver tumors, several complexities have to be taken into account, including accessibility of the target and protection of structures at risk. To allow for safe and efficient treatment under free respiration, in which the liver moves significantly, both planning and execution have to be performed specifically according to the patient's individual breathing. This article reviews the state of the art of liver applications, the tremendous challenges of this field, and approaches to overcome these challenges. This includes modeling of the patient-individual breathing cycle, detection of and adaptation to the actual breathing, and simulation and monitoring of the therapy.

Reliability of regulatory networks and its evolution.

The problem of reliability of the dynamics in biological regulatory networks is studied in the framework of a generalized Boolean network model with continuous timing and noise. Using well-known artificial genetic networks such as the repressilator, we discuss concepts of reliability of rhythmic attractors. In a simple evolution process we investigate how overall network structure affects the reliability of the dynamics. In the course of the evolution, networks are selected for reliable dynamics. We find that most networks can be easily evolved towards reliable functioning while preserving the original function.

BACH: Grand challenge on breast cancer histology images.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Reliability of genetic networks is evolvable.

Control of the living cell functions with remarkable reliability despite the stochastic nature of the underlying molecular networks--a property presumably optimized by biological evolution. We ask here to what extent the ability of a stochastic dynamical network to produce reliable dynamics is an evolvable trait. Using an evolutionary algorithm based on a deterministic selection criterion for the reliability of dynamical attractors, we evolve networks of noisy discrete threshold nodes. We find that, starting from any random network, reliability of the attractor landscape can often be achieved with only a few small changes to the network structure. Further, the evolvability of networks toward reliable dynamics while retaining their function is investigated and a high success rate is found.

Visualization support for the planning of hepatic needle placement.

PURPOSE: Percutaneous image-guided interventions, such as radiofrequency ablation or biopsy, are using needle-shaped instruments which have to be inserted into a target area without penetrating any vital structure. The established planning workflow is based on viewing 2D slices of a pre-interventional CT or MR scan. However, access paths not parallel to the axial plane are often necessary. For such complicated cases, the planning process is challenging and time consuming if solely based on 2D slices. To overcome these limitations while keeping the well-established workflow, we propose a visualization method that highlights less suited paths directly in the 2D visualizations with which the radiologist is familiar. METHODS: Based on a user defined target point and segmentation masks of relevant risk structures, a risk structure map is computed using GPU accelerated volume rendering and projected onto the 2D slices. This visualization supports the user in defining safe linear access paths by selecting a second point directly in the 2D image slices. RESULTS: In an evaluation for 20 liver radiofrequency ablation cases, 3 experienced radiologists stated for 55% of the cases that the visualization supported the access path choice. The visualization support was rated with an average mark of 2.2. For 2 of the 3 radiologists, a significant reduction of the planning duration by 54 and 50% was observed. CONCLUSIONS: The proposed visualization approach can both accelerate the access path planning for radiofrequency ablation in the liver and facilitate the differentiation between safer and less safe paths.

Superstability of the yeast cell-cycle dynamics: ensuring causality in the presence of biochemical stochasticity.

Gene regulatory dynamics are governed by molecular processes and therefore exhibits an inherent stochasticity. However, for the survival of an organism it is a strict necessity that this intrinsic noise does not prevent robust functioning of the system. It is still an open question how dynamical stability is achieved in biological systems despite the omnipresent fluctuations. In this paper we investigate the cell cycle of the budding yeast Saccharomyces cerevisiae as an example of a well-studied organism. We study a genetic network model of 11 genes that coordinate the cell-cycle dynamics using a modeling framework which generalizes the concept of discrete threshold dynamics. By allowing for fluctuations in the process times, we introduce noise into the model, accounting for the effects of biochemical stochasticity. We study the dynamical attractor of the cell cycle and find a remarkable robustness against fluctuations of this kind. We identify mechanisms that ensure reliability in spite of fluctuations: 'Catcher states' and persistence of activity levels contribute significantly to the stability of the yeast cell cycle despite the inherent stochasticity.