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

arXiv: https://arxiv.org/abs/2312.09854 · HTML: https://mlsanity.com/paper/2312.09854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers](https://mlsanity.com/paper/2306.00001/) (2023)
- [Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots](https://mlsanity.com/paper/2205.05748/) (2022)
- [TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation](https://mlsanity.com/paper/2307.07813/) (2023)
- [Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO](https://mlsanity.com/paper/2311.01057/) (2023)
- [M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments](https://mlsanity.com/paper/1811.07738/) (2018)

## Built on by (most cited first, 1 of 1)

- [TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection](https://mlsanity.com/paper/2603.16451/) (2026)

## Abstract (arXiv)

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.