Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis

• URL: http://arxiv.org/abs/2312.09854v3
• Date: Mon, 4 Mar 2024 15:21:18 GMT
• Title: Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis
• Authors: Pietro Bonazzi, Yawei Li, Sizhen Bian, Michele Magno
• Abstract summary: We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with the Sony IMX500. Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of only 72mW. This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments.
• Score: 13.018482089796159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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