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Title: Machine learning for analysing whole-body scans
Author: Valindria, Vanya
ISNI:       0000 0004 7963 7791
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Advances in machine learning techniques have been shown to bring benefit for analysing medical images. Whole-body scans contain multiple organs, which makes the manual annotation harder, hence the amount of data obtained is still limited. Meanwhile, machine learning needs a large amount of annotated data in order to perform well. Image segmentation is a crucial task in medical image analysis and beneficial for clinical diagnosis. One of the segmentation challenges is to infer the automatic segmentation quality between algorithms. Unfortunately, the manual annotations required to compute the segmentation accuracy are sometimes unavailable. In order to tackle this, we propose a new method to predict the segmentation evaluation on a per-case basis without annotated data. Segmenting scans acquired from different imaging centers than the one used for algorithm training, can lead to degraded segmentation results. This problem has brought the necessity of domain adaptation. We studied how to select useful samples from unlabeled target domain for supervised domain adaptation. This framework is shown to be less time-consuming with promising results to be useful in clinical practice. A novel dual-stream network is also introduced to learn useful features on unpaired images from different modalities. This network demonstrates its power in multi-modal learning to segment multiple abdominal organs on both modalities. Additionally, strategies to segment challenging small organs on whole-body scans are investigated. We show that using two-stage networks with weighting schemes is useful to solve class imbalance and multi-scale contextual information. Machine learning is not only helping the work of radiologists with faster automatic segmentation tools, but also leveraging the useful features for alleviating real clinical problems. We believe that our proposed methods could offer an important contribution to the field of medical image analysis; advancing our understanding on how machine-learning approaches could be applied in several problems in whole-body image analysis.
Supervisor: Rueckert, Daniel ; Glocker, Ben Sponsor: Indonesian Endowment Fund for Education
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral