Asha K Kumaraswamy1,2, Punith B Venkategowda1, Chandrashekar M. Patil2, and Robert Grimm3
1Siemens Healthcare Private Limited, Bengaluru, India, 2Vidyavardhaka College of Engineering, Mysuru, India, 3MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
Synopsis
Whole
body diffusion-weighted MR imaging is a promising technique for the evaluation of bone metastases e.g.
in prostate and breast cancer. Segmenting and quantifying tumor burden and treatment
response based on DWI and corresponding ADC has been proposed previously.
However, treatment effects may influence the actual segmentation and signal
intensities. Here, a more reproducible approach would be preferred for
segmenting and analyzing consistent regions of interest also in follow-up
examinations. We present a deep learning method for automatic bone
segmentation, based on T1-weighted imaging or DWI. Feasibility of the
new approach is shown, resulting in significantly reduction in computation time.
Introduction
Diffusion-weighted
MRI (DWI) is extensively used in oncology for disease evaluation. Whole body
diffusion-weighted MRI can be used for tumor staging, bone metastases tumor
assessment from breast, prostate, lung, thyroid,
and melanoma tumors and in treatment response monitoring 1. Segmenting bony structures from whole body
DWI images helps in fast and easy reading of MR images with the purpose of
screening for bone metastases2,3.
In
previous work, apparent diffusion coefficient (ADC) histograms were analyzed by
segmenting bone tumors based on high DWI signal intensity. However, the DWI
signal intensity may vary depending on the treatment response, and a more
reproducible way of segmenting the bone is desired. One common approach for
this is to automatically segment the bones based on T1-weighted Dixon imaging
or on Zero-TE (ZTE) or ultrashort echo-time (UTE) images. However, for
evaluation of the ADC, these methods may suffer from anatomical mismatch due to
imperfect registration between the different image contrast. This work presents
a new approach to benefit from the use of deep learning techniques to segment
the bone directly from diffusion-weighted images.
In
the proposed method, a Convolution Neural Network (CNN)-based model is trained
on weak labels generated by a state-of-the-art template-based bone segmentation
algorithm on T1-weighted Dixon images4. In this work we compared two approaches: training on T1-weighted 3D Dixon water
images and training on DWI. We evaluated the method on 12 whole-body MRI scans
and achieved mean dice scores of 0.866 and 0.812 on water and diffusion images,
respectively. Materials and Methods
Figure
1
presents an overview of the 3D CNN-based model used for bone segmentation. For
training the CNN-based algorithms, whole body diffusion-weighted images at b=50
s/mm² and Dixon water images from 68 subjects were used, which were acquired on
a 3 T MRI scanner (Biograph mMR, Siemens Healthcare, Erlangen, Germany) at a
single institution. The data from 12 additional subjects was used for evaluation.
The reference bone segmentation for each scan was automatically generated by
using prototype software (MR Bone Scan; Siemens Healthcare, Erlangen, Germany) thus
providing “weak labels” for training. This application used a template-based
algorithm on T1-weighted Dixon images for extraction of a bone mask4
that is normally used for estimating positron emission tomography (PET)
attenuation correction maps. Each whole body scan is converted to axial
orientation and reformatted to a dimension of 192x128x384 while maintaining the
aspect ratio.
The image volume is then divided into 6
training data slabs of dimension 192x128x64. Each image’s intensities were
normalized to zero mean and unit standard deviation. Data
augmentation was performed by randomly rotating ±15° with respect to X, Y, Z
axes and translating along X and Y directions. A total of 2096 3D volumes of
both DWI and water images were used for training.
The 3D CNN model
architecture for bone segmentation uses the 3D resized volumes as input. Each Conv
Block of Figure 1 performs a 3D convolution followed by batch normalization and
exponential linear unit activation with negative slope alpha value 1.0. We used
the Xavier normal initializer5 for weights. The network is
implemented using Keras with a TensorFlow backend on a DGX-1 with 2, 16 GB
Tesla V1 GPUs, batch size = 2, and learning rate = 10-4. Dice coefficient
between the network output and target mask was used as loss function. The network
was trained for 200 epochsResults
Visual assessment of
the bone mask showed strong similarities between CNN and template-based method
(Figure 2 and Figure 3). The overall segmentation quality and shape of the bone
regions were properly preserved in both methods.
The
evaluation for segmentation accuracy on 12 whole body MRI scans showed a dice
value of 0.812 on DWI and 0.866 on Dixon water images (Figure 4). The reference
template-based algorithm took 5 to 6 minutes on a CPU, whereas the CNN approach
takes 72 to 80 seconds. CNN launched in a GeForce GTX 1080 Ti GPU took less than 2 seconds.Discussion and Conclusion
This work presents a novel
approach for bone segmentation using CNN trained on results previously
segmented by a registration-based method. These results show that training a
model on weak labels generated by a classical template-based method is a good
approach to obtain comparable results in less time. Due to the variability of
the training data and the power of generalization of the network, this study
shows better segmentation accuracy even on DWI images. Results can be further optimized
and the dependency on the weak labels can be reduced by including the manually
corrected images.
With
the proposed approach, bone segmentation speed is increased by nearly 4 times. This
method can be used to segment bone directly from DWI images and the results can
be used to analyze corresponding ADC images for tumor response. Acknowledgements
All imaging data are courtesy of Dr.
Markus Lentschig, ZEMODI, Bremen.References
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