Deep Learning Based Analysis of Prostate Cancer from MP-MRI

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Perustieteiden korkeakoulu | Master's thesis
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Date

2020-06-16

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Major/Subject

Computer Science

Mcode

SCI3042

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

74+4

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Abstract

The diagnosis of prostate cancer faces a problem with over diagnosis that leads to damaging side effects due to unnecessary treatment. Research has shown that the use of multi-parametric magnetic resonance images to conduct biopsies can drastically help to mitigate the over diagnosis, thus reducing the side effects on healthy patients. This study aims to investigate the use of deep learning techniques to explore computer-aid diagnosis based on MRI as input. Several diagnosis problems ranging from classification of lesions as being clinically significant or not to the detection and segmentation of lesions are addressed with deep learning based approaches. This thesis tackled two main problems regarding the diagnosis of prostate cancer. Firstly, a deep neural network architecture, XmasNet, was used to conduct two large experiments on the classification of lesions. Secondly, detection and segmentation experiments were conducted, first on the prostate and afterward on the prostate cancer lesions. The former experiments explored the lesions through a two-dimensional space, while the latter explored models to work with three-dimensional inputs. For this task, the 3D models explored were the 3D U-Net and a pretrained 3D ResNet-18. A rigorous analysis of all these problems was conducted with a total of two networks, two cropping techniques, two resampling techniques, two crop sizes, five input sizes and data augmentations experimented for lesion classification. While for segmentation two models, two input sizes and data augmentations were experimented. Moreover the experiments were conducted for both sequences independently, and within the lesion classification problem, the experiments were also conducted for both sequences simultaneously. However, while the binary classification of the clinical significance of lesions and the detection and segmentation of the prostate already achieve the desired results (0.870 AUC and 0.915 dice score respectively), the classification of the PIRADS score and the segmentation of lesions still have a large margin to improve (0.664 accuracy and 0.690 dice score respectively). It was also studied how some flaws in the dataset can be addressed to improve the results of all these problems. Further research on the problem is still needed, but nonetheless, this thesis established sufficient ground for future work to be conducted.

Description

Supervisor

Kannala, Juho

Thesis advisor

Akram, Saad

Keywords

deep learning, computer vision, mp-MRI, prostate cancer, 3D segmentation, medical imaging

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https://urn.fi/URN:NBN:fi:aalto-202006213908

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[dipl] Perustieteiden korkeakoulu / SCI