Parameters for CRFs in training and testing.

<p>We use Stanford CRFs with the following settings in the experiment.</p

Testing results on GENIA data by four approaches.

<p>SVM-CRFs¹ refers to the SVM-CRFs without amending and SVM-CRFs² is SVM-CRFs with amending. P, R and F1 are precision, recall, and F1 respectively.</p

Testing results on JNLPBA04 data by four approaches.

<p>SVM- CRFs¹ refers to the SVM-CRFs without amending and SVM-CRFs2 is SVM-CRFs with amending. P, R and F1 are precision, recall, and F₁ respectively.</p

Forward and backward probability squeezing takes the product of the probability obtained by forward inference and the probability obtained by backword inference.

<p>Here and are the transfer probability, while is the probability of taking .</p

Parameters for SVM in training and testing.

<p>We use LIB SVM with the following settings in the experiment. The basis function is exp(-gamma*|u-v|²).</p

A curve with a gap between A and B rather than A and C which is nearer than B.

<p>However connecting A and C is incorrect.</p

Thirty test EM images for segmentation from ISIB 2012.

<p>Thirty test EM images for segmentation from ISIB 2012.</p

Segmentation results for neuronal structures using our approach after boundary amendment.

<p>Segmentation results for neuronal structures using our approach after boundary amendment.</p

Segmentation results for neuronal structures using our approach without boundary amendment.

<p>Segmentation results for neuronal structures using our approach without boundary amendment.</p

Standard labeled results after neuronal structure segmentation provided by ISIB 2012.

<p>Standard labeled results after neuronal structure segmentation provided by ISIB 2012.</p