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Research map / Detection and segmentation / Open-set and few-shot detection

Automated scalable segmentation of neurons from multispectral images

NeurIPS 2016 · Detection and segmentation › Open-set and few-shot detection · approach: Interactive and referring segmentation · 21 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
increase number of imaging color channels from 3 to 5 (simulated data) (ablation)Supervoxel clustering pipeline · Simulated Brainbow, 9 neuronsadjusted Rand indexHelped
increase neuron-color noise sigma1 in the reported range (ablation)Pipeline on simulated data · Simulated Brainbowadjusted Rand indexHurt
overestimate neuron count in clustering (ablation)Clustering step · Simulated Brainbow, 9 neuronsadjusted Rand indexNo clear effect
underestimate neuron count in clustering (ablation)Clustering step · Simulated Brainbow, 9 neuronsadjusted Rand indexHurt
supervoxel merging routine (reduces supervoxel count) (ablation)Supervoxel representation · Real hippocampus Brainbow 1020x1020x225supervoxel count: 62000 to 39000 supervoxelsHelped
aggressive supervoxel merging vs conservative merging (ablation)Supervoxel merging · Simulated Brainbowsegmentation qualityHurt
denoise image stack with BM4D collaborative filteringBrainbow images · Real Brainbow patchescolor noise / edge preservation (qualitative)Helped

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