Objective: To develop and evaluate a machine learning approach for predicting blastocyst viability using automated expansion measurements, clinical variables and image features.
Methods: A convolutional neural network was developed to automatically segment and measure blastocyst cross-sectional area from time-lapse images. We generated expansion curves and extracted features for 315 blastocysts. Various machine learning models were trained to predict biochemical and ongoing pregnancy using expansion, clinical and image-derived features. Model performance was evaluated using cross-validation and an unseen test set.
Results: The segmentation model achieved a Jaccard index of 97.6% on the validation set. Support vector machines using clinical and expansion features achieved the highest performance, with AUCs of 0.71 and 0.70 for predicting biochemical and ongoing pregnancy, respectively, on the test set. Blastocysts resulting in pregnancy expanded significantly faster and reached larger final cross-sectional areas compared to those that did not implant. Key predictive features included expansion rate and maternal age.
Conclusions: Automated quantification of blastocyst expansion dynamics combined with clinical variables enables prediction of implantation potential. Incorporating objective expansion metrics into embryo selection may enhance IVF success rates beyond traditional morphological grading systems.

Prostate cancer is the most commonly diagnosed cancer in the Netherlands. Accurate assessment of the prostate volume (PV) is a crucial step in prostate cancer (PCa) screening and risk-stratification. In standard clinical care, the PV is obtained by measuring the prostate dimensions with the aid of transrectal ultrasound (TRUS). However, rectal examination is characterised with patient discomfort, for which the feasibility of transabdominal ultrasound (TAUS) is explored, as a more accessible and patient-friendly alternative. However, manual PV measurements are prone to inter-observer variability and require operator training. This study aims to improve the accessibility, complexity, and robustness of PV measurements by developing a framework to automatically estimate the PV based on TAUS acquisitions. The primary components of the framework comprise two deep neural networks, developed for prostate segmentation on axial and sagittal TAUS images, and an algorithm that extracts the prostate's diameters on which the PV is calculated.

During this study, a new prostate dataset is developed, comprising sagittal and transverse TAUS image acquisitions of 100 participants, and reference PV measurements based on TRUS and MRI are collected. First, the feasibility of TAUS for manual PV estimation is explored, and the inter-method agreement between TAUS, TRUS and MRI is analysed in Bland Altman diagrams. Additionally, all TAUS acquisitions are assessed on image quality.
Secondly, three deep neural networks (using the nnU-Net framework) are developed to segment the prostate on sagittal and/or axial TAUS images. All models, are trained and validated on TAUS image data of 52 participants. Additionally, an algorithm is designed to predict the prostate diameters when prostate segmentations serve as input. To this extend, the PV is estimated according to the widely used Ellipsoid formula. The proposed algorithm is evaluated on input ground-truth segmentations of 42 participants. Essentially, the segmentation models combined with the proposed algorithm result in a framework to automatically estimate the PV on TAUS. Finally, it is tested on unseen TAUS acquisitions of 17 participants, whereby the predicted PV is compared to reference PV measurements on MRI.

Our results show an average volume difference of 3.0 ± 17.6 ml when manual PV estimation on TAUS is compared to MRI. When manual PV estimation on TRUS is compared to MRI, an average volume difference of 12.3 ± 18.8 ml is obtained. The developed segmentations models segment the middle region of the prostate on TAUS with an average DSC = 0.91 ± 0.06 and DSC = 0.83 ± 0.09 for axial and sagittal TAUS images respectively. When the entire prostate region was evaluated, a lower model performance was observed, whereby the prostate was segmented with a DSC of 0.76 ± 0.09 in the axial imaging-plane and DSC of 0.68 ± 0.21 in the sagittal imaging-plane. The algorithm for automatic diameter extraction showed good correspondence with manually assigned prostate diameters on TAUS.
When the segmentation models and the algorithm are utilised for automatic PV estimation, an average volume difference of 2.5 ± 10.2 ml was observed, compared to MRI reference volumes. Ultimately the PV was predicted with a volume difference < 25 compared to MRI in 14 out of 17 test cases. 

The results of this study show that it is possible to obtain PV measurements using TAUS that are comparable to those obtained with MRI. Moreover, the variability related to PV measurements using TAUS seem unrelated to TAUS image quality, indicating that manual PV measurements can be performed, even when unfavorable patient characteristics limit the image quality. Still, it is important to note that proper operator training for TAUS examination is essential. The proposed framework for automatic PV estimation on TAUS acquisitions shows good correspondence with MRI reference volumes. Thus expanding the possibilities of PCa risk-stratification, whereby robust, and straightforward PV estimations are desired. In order to adopt the framework for standard clinical care, further research is required on a larger cohort to investigate the generalizability of the framework and ensure reliable results on all future patients.

Prostate cancer is a disease with very high prevalence and mortality in the western world. An early accurate diagnosis can increase treatment efficiency. Current diagnosing techniques consist in systematic biopsy sampling. Radiomics can infer tumor's phenotypic differentiations from medical images, providing an accurate guide for biopsy sampling and making personalized treatment plans possible. Radiomics are various features that are extracted from medical images. Subsequently they are applied to train machine learning models that distinguish between healthy or cancerous tissue.
In this thesis a software routine that extracts the most commonly reported MRI prostate cancer radiomic features was built. Then, several classification methods were tried. Results were validated on T2 MRI patient images with confirmed histopathology from two different clinics.