# A Lightweight Modified Adaptive UNet for Nucleus Segmentation

## TL;DR

This paper introduces mA-UNet, a lightweight neural network that improves nucleus segmentation in microscopy images and performs well on hardware with limited resources.

## Contribution

The novel mA-UNet architecture and a data preprocessing strategy for imbalanced datasets are introduced for efficient nucleus segmentation.

## Key findings

- mA-UNet achieved a 95.50% MIoU score, outperforming UNet++ on the 2018 Data Science Bowl dataset.
- The model excels at predicting small nuclei and is less prone to overfitting due to its lightweight design.
- mA-UNet was successfully implemented on an FPGA, showing efficiency and scalability in hardware.

## Abstract

Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. However, it is noteworthy that these models perform well on balanced datasets, where the ratio of background to foreground pixels is equal. Within the realm of microscopy image segmentation, state-of-the-art models often encounter challenges in accurately predicting small foreground entities such as nuclei. Moreover, the majority of these models exhibit large parameter sizes, predisposing them to overfitting issues. To overcome these challenges, this study introduces a novel architecture, called mA-UNet, designed to excel in predicting small foreground elements. Additionally, a data preprocessing strategy inspired by road segmentation approaches is employed to address dataset imbalance issues. The experimental results show that the MIoU score attained by the mA-UNet model stands at 95.50%, surpassing the nearest competitor, UNet++, on the 2018 Data Science Bowl dataset. Ultimately, our proposed methodology surpasses all other state-of-the-art models in terms of both quantitative and qualitative evaluations. The mA-UNet model is also implemented in VHDL on the Zynq UltraScale+ FPGA, demonstrating its ability to perform complex computations with minimal hardware resources, as well as its efficiency and scalability on advanced FPGA platforms.