IMSAHLO: Integrating Multi-Scale Attention and Hybrid Loss Optimization Framework for Robust Neuronal Brain Cell Segmentation

TL;DR

IMSAHLO is a novel deep learning framework that combines multi-scale attention and hybrid loss functions to improve neuronal cell segmentation accuracy in challenging microscopy images, addressing issues like class imbalance and complex cell morphology.

Contribution

The paper introduces IMSAHLO, integrating multi-scale dense blocks, hierarchical attention, and a hybrid loss function with topology-aware components for robust neuronal segmentation.

Findings

Outperforms state-of-the-art models on FNC dataset.

Achieves high precision and F1 scores in dense and sparse cell cases.

Ablation confirms the effectiveness of multi-scale attention and hybrid loss.

Abstract

Accurate segmentation of neuronal cells in fluorescence microscopy is a fundamental task for quantitative analysis in computational neuroscience. However, it is significantly impeded by challenges such as the coexistence of densely packed and sparsely distributed cells, complex overlapping morphologies, and severe class imbalance. Conventional deep learning models often fail to preserve fine topological details or accurately delineate boundaries under these conditions. To address these limitations, we propose a novel deep learning framework, IMSAHLO (Integrating Multi-Scale Attention and Hybrid Loss Optimization), for robust and adaptive neuronal segmentation. The core of our model features Multi-Scale Dense Blocks (MSDBs) to capture features at various receptive fields, effectively handling variations in cell density, and a Hierarchical Attention (HA) mechanism that adaptively focuses on salient morphological features to preserve Region of Interest (ROI) boundary details. Furthermore, we introduce a novel hybrid loss function synergistically combining Tversky and Focal loss to combat class imbalance, alongside a topology-aware Centerline Dice (clDice) loss and a Contour-Weighted Boundary loss to ensure topological continuity and precise separation of adjacent cells. Large-scale experiments on the public Fluorescent Neuronal Cells (FNC) dataset demonstrate that our framework outperforms state-of-the-art architectures, achieving precision of 81.4%, macro F1 score of 82.7%, micro F1 score of 83.3%, and balanced accuracy of 99.5% on difficult dense and sparse cases. Ablation studies validate the synergistic benefits of multi-scale attention and hybrid loss terms. This work establishes a foundation for generalizable segmentation models applicable to a wide range of biomedical imaging modalities, pushing AI-assisted analysis toward high-throughput neurobiological pipelines.