Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Prediction of Pre-Operative Local Staging and Optimising Treatment Response to Neoadjuvant Therapy in Colorectal Cancer.

The presence of abnormal Lymph Nodes (LNs) in patients with colorectal cancer is an essential determinant of prognosis and guides treatment options (surgical and medical). Staging with Computed Tomography (CT) is somewhat inaccurate in determining true nodal status. As a result, either approximate estimates must be made on imaging, or definitive nodal staging determined by surgical resection before recommendations about the risk vs benefit of chemotherapy can be made reliably. Patients with advanced rectal cancer are commonly referred for neoadjuvant therapy as part of standard care treatment protocols based on Magnetic Resonance Imaging (MRI) local staging. Following neoadjuvant therapy, many patients then undergo surgical resection. However, a significant proportion achieve a complete Clinical Response (cCR) with modern neoadjuvant treatment, and these patients are increasingly offered non-operative management and surveillance with the goal of organ preservation. Accurate clinical staging parameters and predictive markers of tumour response may help guide more personalised treatment strategies and identify potential candidates for non-operative management more accurately. Within the past decade, a promising new strategy termed Total Neoadjuvant Therapy (TNT) has been shown to improve compliance with chemotherapy, by delivering this sequentially with chemoradiotherapy prior to surgery in patients with rectal cancer. TNT has the potential to reduce distant failure risk and provide significantly higher rates of pathological Complete Response (pCR) and cCR with an opportunity to manage patients non-operatively, however, optimal treatment sequencing of radiotherapy and chemotherapy remains somewhat unclear. Pre-operative prediction of nodal status in colon cancer, neoadjuvant treatment response in rectal cancer, as well as optimal sequencing of neoadjuvant therapy, represent major areas of weakness in current treatment paradigms in colorectal surgical oncology. Furthermore, they are all areas of active research, and frequently tie in together during Multi-Disciplinary Team meeting (MDT) discussions in clinical practice. The aims of this thesis are: Firstly, to investigate Artificial Intelligence (AI) models for prediction of LN status on preoperative staging CT in patients with colon cancer. Secondly, to identify pre-treatment factors predictive of Complete Response (CR) following neoadjuvant therapy in patients with Locally Advanced Rectal Cancer (LARC), specifically sarcopenia, clinical and biochemical factors. Lastly, to determine whether a Personalised Total Neoadjuvant Therapy (pTNT) protocol with sequencing tailored to the clinical stage at presentation results in better short-term oncological outcomes compared to a uniform protocol for all patients with advanced rectal cancer. To achieve these aims, two meta-analyses were performed to identify the gaps in the field of AI LN detection. The first, focused on the accuracy of deep learning algorithms and radiomics models compared with radiologist assessment in the diagnosis of lymphadenopathy in patients with abdominopelvic malignancies and the second solely focused on colorectal cancer. Subsequently, a deep learning model was developed to assess LN status on staging CT in patients with colon cancer, and the model’s performance was compared with baseline results of a prospective study evaluating the accuracy of preoperative staging. A systemic review and meta-analysis were performed to identify and assess AI segmentation models able to predict sarcopenia using CT scans. Following this, an institutional colorectal cancer database was interrogated to determine if sarcopenia or clinical and biochemical markers were associated with tumour response in patients with LARC. Prospective data was collected on patients in two hospitals who underwent pTNT based on their clinical staging at presentation for the treatment of advanced rectal cancer. A cohort study was performed to summarise tumour response, chemotherapy compliance and the toxicity profile of patients. An additional multicentre retrospective cohort analysis comparing pTNT over a 3-year period to a historical cohort of randomised control trial patients who had extended chemotherapy in the wait period (xCRT) or standard long course Chemoradiotherapy (sCRT) was conducted. The two meta-analyses determined that deep learning assessment of LNs demonstrated the greatest potential for assessment of LN without the need for surgery, with MRI for rectal cancer and CT in colon cancer providing the greatest accuracy. Our clinical studies demonstrated that radiological assessment remains the most effective preoperative method of staging LNs, with histology considered the gold standard. Deep learning assessment using a ResNet-50 framework is limited to very low accuracy and specificity in detecting abnormal LNs when compared to the radiologist’s assessment. It is likely that the poor performance of the deep learning model is attributed to the lack of features extracted from the CT scans. The meta-analysis found that deep learning segmentation models can accurately predict sarcopenia using CT scans. However, sarcopenia was not found to be a predictor of pCR in patients with LARC. The clinical predictors of good tumour response after neoadjuvant therapy for rectal cancer were found to be a clinical T2 stage and Body Mass Index (BMI) ≥25kg/m2. Pre-treatment biochemical markers were not predictive of tumour response after neoadjuvant therapy for rectal cancer. Our research found that over 40% of the patients who underwent pTNT for the treatment of advanced rectal cancer demonstrated a complete response in the primary tumour (pCR and/or cCR) resulting in a high rate of organ preservation. Furthermore, 45% of the patients with stage M1 disease achieved a complete M1 response. Compliance with chemotherapy was over 95% and toxicity was lower than expected. When comparing a pTNT approach with xCRT or sCRT in patients with LARC, there was a significant difference in complete response and cCR rate favouring the pTNT group compared to the xCRT and sCRT groups. In conclusion, these results suggest that a deep learning model with a ResNet-50 framework does not serve as a reliable staging tool for the prediction of LN status using preoperative staging CT in patients with colon cancer. Despite a large volume of research, the ability to predict which patients are likely to achieve a complete response by measuring pre-treatment sarcopenia, clinical and biochemical markers remains elusive. Early results of a pTNT approach tailoring sequencing of neoadjuvant chemotherapy to disease risk at presentation are encouraging and compare favourably to xCRT and sCRT in patients with advanced rectal cancer.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 202

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples despite their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. To help the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we introduce the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modelling frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research