Problem Detect and segment out individual cells in a dense population in microscopic images Approach SIFT? No stable spatial structures Sliding window? Cell shapes highly irregular N-cuts? Not discriminative Needs total # of cells Separate Detection/Segmentation Conventional CRF • The state set is {0,1,…,N}, N is the total number of interest points • The resulting model is unidentiﬁable Maximum posterior probability shared by several states CRF cannot select the correct state assign- ment combination Heterogeneous Conditional Random Field (HCRF): Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images Jiyan Pan 1 , Takeo Kanade 1 , and Mei Chen 2 1 Carnegie Mellon University, 2 Intel Labs Pittsburgh 1 {jiyanpan, tk}@cs.cmu.edu, 2 [email protected] Heterogeneous CRF (HCRF) Before MAP inference Give nodes an arbitrary ordering Restricted propagation rule Each node propagates its node index in turn A node neither accepts nor passes on any state greater than its node index Results Proposed approach: • Extract interest points and features • Classify points into cell or background (detection) • Group points within the same cell (segmentation) • Extend points to regions Remaining Unidentiﬁability After MAP inference Non-maxima suppression rule If a node’s maximum posterior probability is shared by several states, it takes the largest state HCRF is provably complete, irreducible, unique, and sound • Cell Type bovine aortic endothelial cells C2C12 muscle stem cells • For each cell type 10 images for training 10 images for testing • Compare HCRF with separate detection and segmentation conventional CRF Inpu t Separat e HCRF CRF Bovin e C2C12 Joint detection and segmentation outperforms separate detection and segmentation Conventional CRF cannot achieve joint detection and segmentation due to Conclusion Bovi ne C2C1 2 Joint Detection/Segmentation by CRF • Two critical parameters to tune • Cannot recover from detection errors • No mutual enhancement between detection and segmentation

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Heterogeneous Conditional Random Field (HCRF): Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images Jiyan Pan 1 , Takeo Kanade 1 , and Mei Chen 2 - PowerPoint PPT Presentation

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Problem Detect and segment out individual cells in a dense population in microscopic images

Approach SIFT? No stable spatial structures

Sliding window? Cell shapes highly irregular

N-cuts? Not discriminativeNeeds total # of cells

Separate Detection/Segmentation

Conventional CRF• The state set is {0,1,…,N}, N is the total number of interest points• The resulting model is unidentifiable

Maximum posterior probability shared by

several states CRF cannot select the correct state assign- ment combination

Heterogeneous Conditional Random Field (HCRF): Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images

Jiyan Pan1, Takeo Kanade1, and Mei Chen2

1Carnegie Mellon University, 2Intel Labs Pittsburgh 1 {jiyanpan, tk}@cs.cmu.edu, [email protected]

Heterogeneous CRF (HCRF)

Before MAP inference Give nodes an arbitrary ordering Restricted propagation rule

Each node propagates its node index in turn A node neither accepts nor passes on any state greater than its node index

Results

Proposed approach:• Extract interest points and features• Classify points into cell or background (detection)• Group points within the same cell (segmentation)• Extend points to regions

Remaining Unidentifiability

After MAP inferenceNon-maxima suppression rule

If a node’s maximum posterior probability is shared by several states, it takes the largest state

HCRF is provably complete, irreducible, unique, and sound

• Cell Type bovine aortic endothelial cells C2C12 muscle stem cells

• For each cell type 10 images for training 10 images for testing

• Compare HCRF with separate detection and segmentation conventional CRF

Input

Separate

HCRF

CRF

Bovine C2C12