THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

Statistics for Spatial Data.

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

. In this paper we propose a novel way to measure behavioral heterogeneity in a population of stochastic individuals. Our measure is choice-based; it evaluates the probability that, over a randomly selected menu, the sampled choices of two sampled individuals diﬀer. We provide axiomatic foundations for this measure and a decomposition result that separates heterogeneity into its intra- and inter-personal components.

A measure of behavioral heterogeneity

Adaptation refers to the process of changing behavior in response to a variation in the environment. We propose a model of an adaptive individual that contemplates two forces: on the one hand the individual benefits from adopting the ideal response to the new environment, but on the other hand, behavioral change is costly. We lay down the axiomatic foundations of the model. We then study two applications. The first studies a situation where ideal behavior depends on the response of another adaptive individual. The second analyzes the case where the ideal response is influenced by the strategic interaction in a cheap talk-like game. 

A behavioral model of adaptation

Cochlear implantation is a surgical technique which aims to restore hearing in people with deep hearing loss. However, outcomes of the surgery still exhibit a large variability between patients. Among the factors that contribute to variability the most important are morphological differences in anatomical structures between patients and incorrect implant placements. In order to address these issues, it would be desirable to have a functional model of the cochlea which incorporates inter-patients variability and simulate electrode placement. To this end, we present a finite element model which captures the interaction between the cochlear partition, modeled as an elastic solid with finite deformation, and the perilymph fluid, modeled as a compressible, viscous fluid. Numerical results show that the membrane responds to changes in the stimulation frequencies.

Functional simulation of the cochlea for implant optimization

Tracking cells is one of the main challenges in biology, as it often requires time-consuming annotations and the images can have a low signal-to-noise ratio while containing a large number of cells. Here we present two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks. A straightforward approach is described using Fiji, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence. Using ilastik, a classifier is trained through manual annotations to both detect cells over the background and be able to recognize false detections and merging cells. We describe these two methods in a step-by-step fashion, using as example a time-lapse microscopy movie of HeLa cells.

2D + Time Object Tracking Using Fiji and ilastik.

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

https://www.tandfonline.com/doi/pdf/10.3109/10929088.2010.496176

Miguel Ángel González Ballester

Papers

A measure of behavioral heterogeneity

A behavioral model of adaptation

Functional simulation of the cochlea for implant optimization

2D + Time Object Tracking Using Fiji and ilastik.

Automated cement segmentation in vertebroplasty.