Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints of reaching the goal and avoiding unsafe states, as the structure of this new optimization problem is incompatible with current methods. Instead, a surrogate problem is solved where all objectives are combined with a weighted sum. However, this surrogate objective results in suboptimal policies that do not directly minimize the cumulative cost. In this work, we propose RC-PPO, a reinforcement-learning-based method for solving the minimum-cost reach-avoid problem by using connections to Hamilton-Jacobi reachability. Empirical results demonstrate that RC-PPO learns policies with comparable goal-reaching rates to while achieving up to 57% lower cumulative costs compared to existing methods on a suite of minimum-cost reach-avoid benchmarks on the Mujoco simulator. The project page can be found at https://oswinso.xyz/rcppo/.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyperparameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deeplearning algorithm that not only exceeds existing methods, but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. Cell segmentation is a fundamental task that is universally required for biological image analysis across a large number of different experimental settings and imaging modalities. For example, in multiplexed fluorescence image-based cancer microenvironment analysis, cell segmentation is the prerequisite for the identification of tumor sub-types, composition, and organization, which can lead to important biological insights [1]-[3]. However, the development of a universal and automatic cell segmentation technique continues to pose significant challenges due to the extensive diversity observed in microscopy images. This diversity arises from variations in cell origins, microscopy types, staining techniques, and cell morphologies. Recent advances [4], [5] have successfully demonstrated the feasibility of automatic and precise cellular segmentation for specific microscopy image types and cell types, such as fluorescence and mass spectrometry images [6], [7], differential interference contrast images of platelets [8], bacteria images [9] and yeast images [10], [11], but the selection of appropriate segmentation models remains a non-trivial task for non-expert users in conventional biology laboratories. Efforts have been made towards the development of generalized cell segmentation algorithms [9], [12], [13]. However, these algorithms were primarily trained using datasets consisting of gray-scale images and two-channel fluorescent images, lacking the necessary diversity to ensure robust generalization across a wide range of imaging modalities. For example, the segmentation models have struggled to perform effectively on RGB images, such as bone marrow aspirate slides stained with Jenner-Giemsa. Furthermore, these models often require manual selection of both the model type and the specific image channel to be segmented, posing challenges for biologists with limited computational expertise. Biomedical image data science competitions have emerged as an effective way to accelerate the development of cutting-edge algorithms [14], [15].

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. Abdominal organs are high cancer incidence areas, such as liver cancer, kidney cancer, pancreas cancer, and gastric cancer [1]. Computed Tomography (CT) scanning has been a major imaging technology for the diagnosis and treatment of abdominal cancer because it can yield important prognostic information with fast imaging speed for cancer patients, which has been recommended by many clinical treatment guidelines. In order to quantify abdominal organs, radiologists and clinicians need to manually delineate organ boundaries in each slice of the 3D CT scans [2], [3]. However, manual segmentation is time-consuming and inherently subjective with inter-and intra-expert variability.

The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption.In this paper, we focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a state-of-the-art MO-MAPF algorithm. We show that the standard splitting strategy used by MO-CBS can lead to duplicate search nodes and hence can duplicate the search effort that MO-CBS needs to make. To address this issue, we propose two new splitting strategies for MO-CBS, namely cost splitting and disjoint cost splitting. Our theoretical results show that, when combined with either of these two new splitting strategies, MO-CBS maintains its completeness and optimality guarantees. Our experimental results show that disjoint cost splitting, our best splitting strategy, speeds up MO-CBS by up to two orders of magnitude and substantially improves its success rates in various settings.