The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Time-resolved single-crystal X-ray crystallography

In this chapter the development of time-resolved crystallography is traced from its beginnings more than 30 years ago. The importance of being able to “watch” chemical processes as they occur rather than just being limited to three-dimensional pictures of the reactant and final product is emphasised, and time-resolved crystallography provides the opportunity to bring the dimension of time into the crystallographic experiment. The technique has evolved in time with developments in technology: synchrotron radiation, cryoscopic techniques, tuneable lasers, increased computing power and vastly improved X-ray detectors. The shorter the lifetime of the species being studied, the more complex is the experiment. The chapter focusses on the results of solid-state reactions that are activated by light, since this process does not require the addition of a reagent to the crystalline material and the single-crystalline nature of the solid may be preserved. Because of this photoactivation, time-resolved crystallography is often described as “photocrystallography”. The initial photocrystallographic studies were carried out on molecular complexes that either underwent irreversible photoactivated processes where the conversion took hours or days. Structural snapshots were taken during the process. Materials that achieved a metastable state under photoactivation and the excited (metastable) state had a long enough lifetime for the data from the crystal to be collected and the structure solved. For systems with shorter lifetimes, the first time-resolved results were obtained for macromolecular structures, where pulsed lasers were used to pump up the short lifetime excited state species and their structures were probed by using synchronised X-ray pulses from a high-intensity source. Developments in molecular crystallography soon followed, initially with monochromatic X-ray radiation, and pump-probe techniques were used to establish the structures of photoactivated molecules with lifetimes in the micro- to millisecond range. For molecules with even shorter lifetimes in the sub-microsecond range, Laue diffraction methods (rather than using monochromatic radiation) were employed to speed up the data collections and reduce crystal damage. Future developments in time-resolved crystallography are likely to involve the use of XFELs to complete “single-shot” time-resolved diffraction studies that are already proving successful in the macromolecular crystallographic field.</p

The PLATO mission

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2REarth) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases

The PLATO mission

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2R ) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases

Photocatalysis by inorganic solid materials: Revisiting its definition, concepts, and experimental procedures

This review has been written in order to clarify fundamental aspects of photocatalysis, an important subject in inorganic and material chemistry, not to present a list of studies on photocatalysis reported so far, since it seems rather difficult to make a complete review by introducing all or a large part of the reported studies on photocatalysis of relatively long history. This review is based on the author's experience in studies on photocatalysis and topics are limited to so-called semiconductor photocatalysis; definition and examples of photocatalysis, its principle and kinetics, visible light-induced photocatalysis, and design of active photocatalysts are discussed in detail