OrganoidTracker: efficient cell tracking using machine learning and manual error correction

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

A Better Algorithm for Societal Tradeoffs

In the societal tradeoffs problem, each agent perceives certain quantitative tradeoffs between pairs of activities, and the goal is to aggregate these tradeoffs across agents. This is a problem in social choice; specifically, it is a type of quantitative judgment aggregation problem. A natural rule for this problem was axiomatized by Conitzer et al. [AAAI 2016]; they also provided several algorithms for computing the outcomes of this rule. In this paper, we present a significantly improved algorithm and evaluate it experimentally. Our algorithm is based on a tight connection to minimum-cost flow that we exhibit. We also show that our algorithm cannot be improved without breakthroughs on min-cost flow.

Algorithm for Searching an Equilibrium in a Routing Game with Piecewise Constant Cost Functions

We consider nonatomic routing games which are used to model transportation systems with a large number of agents and suggest an algorithm to search for the user equilibrium in such games, which is a generalization of the notion of Nash equilibrium. In general, finding a user equilibrium in routing games is computationally a hard problem. We consider the following subclass of routing games: games with piecewise constant cost functions, and construct an algorithm finding equilibrium in such games. This algorithm is based on the potential function method and the method of piecewise linear (PWL) costs enumeration which finds min-cost flow in a network with PWL cost functions. If each cost function increases, then the complexity of our algorithm is polynomial in the parameters of the network. But if some cost functions have decreasing points, then the complexity is exponential by the number of such points.