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 two low-power edge vision platforms, namely Sony IMX500, which has an in-sensors processor, and Sony Spresense, a low-power multi-core ARM Cortex-M microcontroller. 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 of only 1.9 ms and energy consumption of only 5.7 mJ. We compare the proposed network with outperforming existing networks on various platforms by a factor of 75x (compared to ERFNet). The network architecture 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. This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments.

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