Nome Cognome, assoc.prof. ABC Dept.

Field of application

Brain Tumor

200000 deaths in the world per year (WHO, 2015)

Surgical

Resection

Maria Tirindelli

Low Grade High Grade

Pharmaceutical

treatmentsRadiotherapy


Brain Tumor

Maria Tirindelli


Pharmaceutical

treatmentsSurgical

ResectionRadiotherapy

Low Grade High Grade

200000 deaths in the world per year (WHO, 2015)


Pharmaceutical treatments Oral or intravascular drug

administration

Limitation: Presence of the Blood-Brain barrier

Low drug concentration at the lesion

Lower effectiveness of the treatment

Maria Tirindelli



Automatic catheter insertion for

Local Drug Release

Higher drug concentration at

the lesion

Higher effectiveness of the

treatement

Catheter tracking on ultrasound volumes

Maria Tirindelli



Shadowing effects

Low Signal to Noise Ratio

Low resolution

Ultrasound data

3D in real time

Information about soft

tissues

No ionizing radiation

Maria Tirindelli



Shadowing effects


Low resolution

Ultrasound data

Maria Tirindelli


3D in real time


tissues



Shadowing effects


Low resolution

Ultrasound data

Necessity of method for catheter

tracking on ultrasound volumes

Robust against noise and

artifacts

3D

Fast

Maria Tirindelli


3D in real time


tissues



Biopsy needle tracking (Uhervicik et al., 2013)

Maria Tirindelli

State of the Art



Maria Tirindelli

State of the Art

Ablation catheter tracking

(Bauer et al., 2016)



(Milletari et al., 2016)

Prostate segmentation: VNet

Maria Tirindelli

State of the Art

Ablation catheter tracking

(Bauer et al., 2016)


Goal of the thesis

Method for catheter tracking on

ultrasound volumes

Robust against noise and artifacts

3D

Fast

CONVOLUTIONAL NEURAL

NETWORKS

Maria Tirindelli


Convolutional Neural Networks

CONVOLUTIONAL LAYER

DOWNSAMPLING ACTIVATIONDECONVOLUTIONAL

LAYER

CONVOLUTIONAL LAYER

ACTIVATION

DOWNSAMPLINGBRANCH

UPSAMPLINGBRANCH

Maria Tirindelli


*

CONVOLUTIONAL LAYER


Maria Tirindelli



Maria Tirindelli

*

*


Stride > 1

*

+ DOWNSAMPLING LAYER


Maria Tirindelli

*


+


Maria Tirindelli

**

*


+ACTIVATION

LAYER


Maria Tirindelli

**


+


Maria Tirindelli

**


+

Stride < 1

*

DECONVOLUTIONLAYER


Maria Tirindelli

**


+


Maria Tirindelli

***

Stride < 1

*



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+ ***+*


SKIP CONNECTION: concatenation


Maria Tirindelli

+ ***+*


+* * * * +

SKIP CONNECTION: concatenation

CONVOLUTIONAL NEURAL NETWORK

Trainable parameters can be automatically tuned in orderto perform segmentation

TRAINABLE PARAMETERS filters’ coefficients


Maria Tirindelli


NEURAL NETWORK

Input Batch

SIGMOID LAYER LOSS FUNCTION

𝑻𝑷𝒊𝒕 = 𝑻𝑷𝒊

𝒕−𝟏 − 𝜶 ∙𝝏𝑳𝒐𝒔𝒔

𝝏𝑻𝑷𝒊

Learning rate

Convolutional Neural Networks - TRAINING

Maria Tirindelli


VNet

Maria Tirindelli

SIGMOID LAYER


VNet

Maria Tirindelli

Convolutionallayer

SIGMOID LAYER


VNet

Maria Tirindelli

Convolutionallayer

Short-cuts

SIGMOID LAYER


VNet

Maria Tirindelli

Convolutionallayer

Downsamplinglayer

Short-cuts

SIGMOID LAYER


VNet

Maria Tirindelli

Convolutionallayer

Downsamplinglayer

Short-cuts

Activationlayer

SIGMOID LAYER


VNet

Maria Tirindelli

Convolutionallayer

Downsamplinglayer

Deconvolutionlayer

Short-cuts

Activationlayer

SIGMOID LAYER


Workflow

Ultrasound

data

acquisition

Preprocessing

Vnet

Implementation

In tensorflow

Network training

and

fine tuning

Performance

evalutation

Maria Tirindelli


1. Ultrasound volumes acquired with the EDEN catheter

inserted in an Agar phantom

2. Image cropping to 128 x 128 x 128

3. Mean subtraction and division by the standard deviation

• 3 dataset (Training, Validation, Test)

• 240 x 240 x 274 voxels

• Manually segmented

Data acquisition and preprocessing

Maria Tirindelli


Workflow

Ultrasound

data

acquisition

Preprocessing

Vnet

Implementation

In tensorflow

Network training

and

fine tuning

Performance

evalutation

Optimized implementation

of the 3D convolution

Easier deployment of the

architecture

Maria Tirindelli


Workflow

Ultrasound

data

acquisition

Preprocessing

Vnet

Implementation

In tensorflow

Network training

and

fine tuning

Performance

evalutation

Maria Tirindelli


Network training and fine tuning

Batch sizeNEURAL NETWORK SIGMOID LAYER

LOSS FUNCTION

Input Batch

Maria Tirindelli



Batch size

Activation Function

NEURAL NETWORK SIGMOID LAYERLOSS FUNCTION

Input Batch

Maria Tirindelli



Batch size

Activation Function

Loss function


Input Batch

𝑳𝒐𝒔𝒔 = 𝑷𝑾 ∙ 𝒍 ∙ 𝐥𝐨𝐠(𝒚) + 𝑵𝑾 ∙ (𝟏 − 𝒍) ∙ 𝐥𝐨𝐠(𝟏 − 𝒚)

Network outputGround Truth value

Maria Tirindelli



Batch size

Activation Function

Loss function

Optimizer → Adam Optimizer


Input Batch

𝑳𝒐𝒔𝒔 = 𝑷𝑾 ∙ 𝒍 ∙ 𝐥𝐨𝐠(𝒚) + 𝑵𝑾 ∙ (𝟏 − 𝒍) ∙ 𝐥𝐨𝐠(𝟏 − 𝒚)

Network outputGround Truth value

Maria Tirindelli


Workflow

Ultrasound

data

acquisition

Preprocessing

Vnet

Implementation

In tensorflow

Network training

and

fine tuning

Performance

evalutation

Maria Tirindelli


Metrics

A = segmented volumeB= Ground Truth

Maria Tirindelli


Comparison with Frangi filter + SVM

Maria Tirindelli


Results

Batch size Activation function

Maria Tirindelli

PreLu

tanh


Results - Comparison with Frangi filter + SVM

Ground Truth FF + SVM CNN

Maria Tirindelli


Results – Correlation with catheters’ depth

Maria Tirindelli


Conclusion

Tensorflow implementation of VNet

Acquisition of an ultrasound dataset imaging the EDEN

catheter

Eveluation of the network with different

• Batch size

• Activation functions

Comparison with a method based on Frangi filtering + SVM

Maria Tirindelli


Future work

Improve annotation quality → simultaneous CT acquisition

Train the network with the whole volume/automatic selection

of the ROI based on the previous frame and/or on the

planned trajectory

Exploit time information → multichannel CNN, RNN

(Recurrent Neural Network), Kalman filter

Network training on a more challenging dataset (animal

tissue, human brain)

Maria Tirindelli


Acknowledgement

Maria Tirindelli

neural network approach for catheter firma convenzione

Documents