Automatic segmentation of whole-body bone scintigrams as a preprocessing step for computer assisted diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor quality images and artifacts necessitate that algorithms use su±cient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. We present a robust knowledge based methodology for detecting reference points of the main skeletal regions that simultaneously processes anterior and posterior whole-body bone scintigrams. Expert knowledge is represented as a set of parameterized rules which are used to support standard image processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our knowledge based segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is used for automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artifacts necessitate that algorithms use su±cient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians

A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease

Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution. In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data. To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD. The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs. The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms. In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus

Volumetric Quantification of Metastatic Burden from SPECT/CT Images

Bone scintigraphy images are used to investigate the presence of metastases in patients with prostate cancer. An analysis of these images indicates the proportion of cancer in the bones; the Bone Scan Index (BSI). This value is an important prognostic factor used to predict the future for the patients. It is common to extend the examination with a combination of SPECT and CT images to be able to study possible metastases in more detail. The aim of this thesis is to integrate the information from SPECT and CT images to get a more accurate calculation of the BSI. The strategy has been to use the analysis of the bone scintigraphy images as an initialization of a segmentation of metastases in the SPECT images. The CT images are used to produce a simple segmentation of the bones. The problem is divided into two main parts; image registration and segmentation of metastases. The image registration is needed to align the coordinate system of the bone scintigraphy images and the SPECT images. The Morphon method has been chosen and the results are good; twelve of fifteen tested registrations are classified as successful. A combination of a more robust method to find start guesses and a more generous transformation model would probably improve the results even further. Seeded region growing has been chosen as the segmentation algorithm. An implementation with an automatic termination criterion has been created and the results seem promising. The conclusions are that the Morphon method works fine for registration of the images and that the strategy of initializing a segmentation gives good results. In this work, the size of the segmentation was not used to update the BSI but the results can still be useful. Another goal was to facilitate navigation between different types of images by aligning them to one coordinate system. A user interface has been created to reduce the amount of time spent by doctors and biomedical scientists to navigate through the images searching for possible metastases

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Registration and Segmentation of Multimodality Images for Post Processing of Skeleton in Preclinical Oncology Studies

Advancements in medical imaging techniques provide biomedical researchers with quality anatomical and functional information inside preclinical subjects in the fields of cancer, osteopathic, cardiovascular, and neurodegenerative research. The throughput of the preclinical imaging studies is a critical factor which determines the pace of small animal medical research. The time involved in manual analysis of large amount of imaging data prior to data interpretation by the researcher, limits the number of studies in a time frame. In the proposed solution, an automated image segmentation method was used to segment individual vertebrae in mice. Individual vertebrae of MOBY atlas were manually segmented and registered to the CT data. The PET activity for L1-L5 vertebrae was measured by applying the CT registered atlas vertebrae ROI. The algorithm was tested on three datasets from a PET/CT bone metastasis study using 18F-NaF radiotracer. The algorithm was found to reduce the analysis time threefold with a potential to further reduce the automated analysis time by use of computer system with better specification to run the algorithm. The manual analysis value can vary each time the analysis is performed and is dependent on the individual performing the analysis. Also the error percent was recorded and showed an increasing trend as the analysis moves down the spine from skull to caudal vertebrae. This method can be applied to segment the rest of the bone in the CT data and act as the starting point for the registration of the soft tissues

Implementation of an enhanced planar processing protocol in clinical practice

Introdução: A cintigrafia óssea é um dos exames mais frequentes em Medicina Nuclear. Esta modalidade de imagem médica requere um balanço apropriado entre a qualidade de imagem e a dose de radiação, ou seja, as imagens obtidas devem conter o número mínimo de contagem necessárias, para que apresentem qualidade considerada suficiente para fins diagnósticos. Objetivo: Este estudo tem como principal objetivo, a aplicação do software Enhanced Planar Processing (EPP), nos exames de cintigrafia óssea em doentes com carcinoma da mama e próstata que apresentam metástases ósseas. Desta forma, pretende-se avaliar a performance do algoritmo EPP na prática clínica em termos de qualidade e confiança diagnóstica quando o tempo de aquisição é reduzido em 50%. Material e Métodos: Esta investigação teve lugar no departamento de Radiologia e Medicina Nuclear do Radboud University Nijmegen Medical Centre. Cinquenta e um doentes com suspeita de metástases ósseas foram administrados com 500MBq de metilenodifosfonato marcado com tecnécio-99m. Cada doente foi submetido a duas aquisições de imagem, sendo que na primeira foi seguido o protocolo standard do departamento (scan speed=8 cm/min) e na segunda, o tempo de aquisição foi reduzido para metade (scan speed=16 cm/min). As imagens adquiridas com o segundo protocolo foram processadas com o algoritmo EPP. Todas as imagens foram submetidas a uma avaliação objetiva e subjetiva. Relativamente à análise subjetiva, três médicos especialistas em Medicina Nuclear avaliaram as imagens em termos da detetabilidade das lesões, qualidade de imagem, aceitabilidade diagnóstica, localização das lesões e confiança diagnóstica. No que respeita à avaliação objetiva, foram selecionadas duas regiões de interesse, uma localizada no terço médio do fémur e outra localizada nos tecidos moles adjacentes, de modo a obter os valores de relação sinal-ruído, relação contraste-ruído e coeficiente de variação. Resultados: Os resultados obtidos evidenciam que as imagens processadas com o software EPP oferecem aos médicos suficiente informação diagnóstica na deteção de metástases, uma vez que não foram encontradas diferenças estatisticamente significativas (p>0.05). Para além disso, a concordância entre os observadores, comparando essas imagens e as imagens adquiridas com o protocolo standard foi de 95% (k=0.88). Por outro lado, no que respeita à qualidade de imagem, foram encontradas diferenças estatisticamente significativas quando se compararam as modalidades de imagem entre si (p≤0.05). Relativamente à aceitabilidade diagnóstica, não foram encontradas diferenças estatisticamente significativas entre as imagens adquiridas com o protocolo standard e as imagens processadas com o EPP software (p>0.05), verificando-se uma concordância entre os observadores de 70.6%. Todavia, foram encontradas diferenças estatisticamente significativas entre as imagens adquiridas com o protocolo standard e as imagens adquiridas com o segundo protocolo e não processadas com o software EPP (p≤0.05). Para além disso, não foram encontradas diferenças estatisticamente significativas (p>0.05) em termos de relação sinal-ruído, relação contraste-ruído e coeficiente de variação entre as imagens adquiridas com o protocolo standard e as imagens processadas com o EPP. Conclusão: Com os resultados obtidos através deste estudo, é possível concluir que o algoritmo EPP, desenvolvido pela Siemens, oferece a possibilidade de reduzir o tempo de aquisição em 50%, mantendo ao mesmo tempo uma qualidade de imagem considerada suficiente para fins de diagnóstico. A utilização desta tecnologia, para além de aumentar a satisfação por parte dos doentes, é bastante vantajosa no que respeita ao workflow do departamento

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society