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

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

abstract: Background This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from [superscript 18]F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. Results For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN’s sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. Conclusions The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.The electronic version of this article is the complete one and can be found online at: https://ejnmmires.springeropen.com/articles/10.1186/s13550-017-0260-

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

The number of people with lung cancer has reached approximately 2.09 million people worldwide. Out of 9.06 million cases of death, 1.76 million people die due to lung cancer. Lung cancer can be automatically identified using a computer-aided diagnosis system (CAD) such as image processing. The steps taken for early detection are pre-processing feature extraction, and classification. Pre-processing is carried out in several stages, namely grayscale images, noise removal, and contrast limited adaptive histogram equalization. This image feature extracted using GLCM and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN). This research aims to obtain the best neural network model to classify lung cancer a. Based on training time and accuracy, the best method of FFNN is kernel extreme learning machine (KELM), with a training time of 12 seconds and an accuracy of 93.45%, while the best method of FBNN is Backpropagation with a training time of 18 minutes 04 seconds and an accuracy of 97.5%

Anatomical Variation and Clinical Diagnosis

In the anatomical sciences, it has long been recognized that the human body displays a range of morphological patterns and arrangements, often termed “anatomical variation”. Variations are relatively common throughout the body and may cause or contribute to significant medical conditions. An understanding of normal anatomical variation is vital for performing a broad range of surgical and other medical procedures and treatment modalities. However, despite their importance to effective diagnosis and treatment, such variations are often overlooked in medical school curricula and clinical practice. Recent advances in imaging techniques and a renewed interest in variation in dissection-based gross anatomy laboratories have facilitated the identification of many such variants. The aim of this Special Issue of Diagnostics is to highlight previously under-recognized anatomical variations and to discuss them in a clinical context. In particular, this Special Issue focuses on variants that have specific implications for diagnosis and treatment and explores their potential consequences. The scope of this Special Issue includes studies on gross anatomy, radiology, surgical anatomy, histology, and neuroanatomy

Scaling Up Medical Visualization : Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication

Medisinsk visualisering er en av de mest applikasjonsrettede områdene av visualiseringsforsking. Tett samarbeid med medisinske eksperter er nødvendig for å tolke medisinsk bildedata og lage betydningsfulle visualiseringsteknikker og visualiseringsapplikasjoner. Kreft er en av de vanligste dødsårsakene, og med økende gjennomsnittsalder i i-land øker også antallet diagnoser av gynekologisk kreft. Moderne avbildningsteknikker er et viktig verktøy for å vurdere svulster og produsere et økende antall bildedata som radiologer må tolke. I tillegg til antallet bildemodaliteter, øker også antallet pasienter, noe som fører til at visualiseringsløsninger må bli skalert opp for å adressere den økende kompleksiteten av multimodal- og multipasientdata. Dessuten er ikke medisinsk visualisering kun tiltenkt medisinsk personale, men har også som mål å informere pasienter, pårørende, og offentligheten om risikoen relatert til visse sykdommer, og mulige behandlinger. Derfor har vi identifisert behovet for å skalere opp medisinske visualiseringsløsninger for å kunne håndtere multipublikumdata. Denne avhandlingen adresserer skaleringen av disse dimensjonene i forskjellige bidrag vi har kommet med. Først presenterer vi teknikkene våre for å skalere visualiseringer i flere modaliteter. Vi introduserer en visualiseringsteknikk som tar i bruk små multipler for å vise data fra flere modaliteter innenfor et bildesnitt. Dette lar radiologer utforske dataen effektivt uten å måtte bruke flere sidestilte vinduer. I det neste steget utviklet vi en analyseplatform ved å ta i bruk «radiomic tumor profiling» på forskjellige bildemodaliteter for å analysere kohortdata og finne nye biomarkører fra bilder. Biomarkører fra bilder er indikatorer basert på bildedata som kan forutsi variabler relatert til kliniske utfall. «Radiomic tumor profiling» er en teknikk som genererer mulige biomarkører fra bilder basert på første- og andregrads statistiske målinger. Applikasjonen lar medisinske eksperter analysere multiparametrisk bildedata for å finne mulige korrelasjoner mellom kliniske parameter og data fra «radiomic tumor profiling». Denne tilnærmingen skalerer i to dimensjoner, multimodal og multipasient. I en senere versjon la vi til funksjonalitet for å skalere multipublikumdimensjonen ved å gjøre applikasjonen vår anvendelig for livmorhalskreft- og prostatakreftdata, i tillegg til livmorkreftdataen som applikasjonen var designet for. I et senere bidrag fokuserer vi på svulstdata på en annen skala og muliggjør analysen av svulstdeler ved å bruke multimodal bildedata i en tilnærming basert på hierarkisk gruppering. Applikasjonen vår finner mulige interessante regioner som kan informere fremtidige behandlingsavgjørelser. I et annet bidrag, en digital sonderingsinteraksjon, fokuserer vi på multipasientdata. Bildedata fra flere pasienter kan sammenlignes for å finne interessante mønster i svulstene som kan være knyttet til hvor aggressive svulstene er. Til slutt skalerer vi multipublikumdimensjonen med en likhetsvisualisering som er anvendelig for forskning på livmorkreft, på bilder av nevrologisk kreft, og maskinlæringsforskning på automatisk segmentering av svulstdata. Som en kontrast til de allerede fremhevete bidragene, fokuserer vårt siste bidrag, ScrollyVis, hovedsakelig på multipublikumkommunikasjon. Vi muliggjør skapelsen av dynamiske og vitenskapelige “scrollytelling”-opplevelser for spesifikke eller generelle publikum. Slike historien kan bli brukt i spesifikke brukstilfeller som kommunikasjon mellom lege og pasient, eller for å kommunisere vitenskapelige resultater via historier til et generelt publikum i en digital museumsutstilling. Våre foreslåtte applikasjoner og interaksjonsteknikker har blitt demonstrert i brukstilfeller og evaluert med domeneeksperter og fokusgrupper. Dette har ført til at noen av våre bidrag allerede er i bruk på andre forskingsinstitusjoner. Vi ønsker å evaluere innvirkningen deres på andre vitenskapelige felt og offentligheten i fremtidige arbeid.Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data. This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition. Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.Doktorgradsavhandlin