CASAband: Easy-to-Wear Textile Wristband using Shape Memory Alloy Actuators for Spatial and Temporal Haptic Feedback

Haptic interfaces for the wrist and forearm offer an attractive alternative to hand-worn devices as they are simple to wear, leave the hands free for interaction with the real world, and interfere minimally with natural arm motions. To be useful in real-world settings, however, such devices must balance functionality, wearability and comfort, all while being fully untethered with minimal mass and volume. In this work, we present CASAband, a haptic wristband that integrates compliant amplified shape memory alloy actuators (CASA) into a multi-layered textile wristband to deliver spatial and temporal haptic feedback. CASAband operates completely untethered, generates no noise, and has a total mass of 63 g. The device incorporates four actuators that can generate up to 1.7 N of blocked force and 3.2 mm of free displacement with an operating bandwidth ranging from 1.34-6.59 Hz depending on the applied voltage. We conducted a perceptual study and determined that users could identify the location of a single haptic cue around their wrist and discriminate among several patterned cues with over 90% accuracy on average, highlighting that CASAband can be a suitable wearable interface to deliver information for real-world guidance and navigation tasks. To highlight the potential use cases for CASAband, we conducted two demonstrations: a pick and place task where the user relied only on haptic communication from a moderator, and an outdoor pedestrian navigation task where the user relied only on directional cues on the wrist. CASAband is one of the first haptic interfaces that balances the tradeoff between form and function and presents new opportunities for haptic feedback in the real world

One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation

Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome this through per-instance prompting at a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of densely packed instances. We introduce Group Prompting, a new paradigm that shifts interactive segmentation from per-instance $O(N)$ to per-type $O(T)$, where a single click per cell type suffices to segment all instances of that type. Our key observation is that the frozen image encoder of the Segment Anything Model (SAM) already clusters same-type cells in its feature space before any prompt is given. Exploiting this property, we propose Chain-of-Prompts (CoP), a training-free framework that recursively expands a single user click by (1) identifying reliable same-type locations through non-parametric gating of multi-scale encoder features, and (2) selecting the most spatially distant reliable point as the next prompt to maximize coverage. On three cell-type-annotated benchmarks, CoP with one click per type retains over 90% of per-instance performance and surpasses fully-supervised methods without any additional training. On four morphologically homogeneous benchmarks, a single click retains over 99%. Project Page: https://shjo-april.github.io/Chain-of-Prompts/

COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://github.com/shjo-april/COIN.