  This paper addresses the growing interest in deploying deep learning models
directly in-sensor. We present "Q-Segment", a quantized real-time segmentation
algorithm, and conduct a comprehensive evaluation on a low-power edge vision
platform with an in-sensors processor, the Sony IMX500. One of the main goals
of the model is to achieve end-to-end image segmentation for vessel-based
medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves
ultra-low inference time in-sensor only 0.23 ms and power consumption of only
72mW. We compare the proposed network with state-of-the-art models, both float
and quantized, demonstrating that the proposed solution outperforms existing
networks on various platforms in computing efficiency, e.g., by a factor of 75x
compared to ERFNet. The network employs an encoder-decoder structure with skip
connections, and results in a binary accuracy of 97.25% and an Area Under the
Receiver Operating Characteristic Curve (AUC) of 96.97% on the CHASE dataset.
We also present a comparison of the IMX500 processing core with the Sony
Spresense, a low-power multi-core ARM Cortex-M microcontroller, and a
single-core ARM Cortex-M4 showing that it can achieve in-sensor processing with
end-to-end low latency (17 ms) and power concumption (254mW). This research
contributes valuable insights into edge-based image segmentation, laying the
foundation for efficient algorithms tailored to low-power environments.
