Quality Control of Neuron Reconstruction Based on Deep Learning
This addresses the need for efficient quality control in neuron reconstruction to reduce manual annotation effort, though it is incremental as it applies existing deep learning techniques to a specific domain problem.
The paper tackles the problem of ensuring quality in neuron reconstructions by proposing a deep learning method that detects errors with 74.7% accuracy and only 1.4% false alerts, providing both evaluation and precise error localization.
Neuron reconstruction is essential to generate exquisite neuron connectivity map for understanding brain function. Despite the significant amount of effect that has been made on automatic reconstruction methods, manual tracing by well-trained human annotators is still necessary. To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper. By formulating the quality control problem into a binary classification task regarding each single point, the proposed approach overcomes the technical difficulties resulting from the large image size and complex neuron morphology. Not only it provides the evaluation of reconstruction quality, but also can locate exactly where the wrong tracing begins. This work presents one of the first comprehensive studies for whole-brain scale quality control of neuron reconstructions. Experiments on five-fold cross validation with a large dataset demonstrate that the proposed approach can detect 74.7% errors with only 1.4% false alerts.