IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

Efficiency of dry bone inspection compared with two-dimensional os coxal images for age estimation in a Thai population

The auricular surface and pubic symphysis are commonly used in age estimation. This study aimed to compare the results of age estimation between dry bones and 2D images of the os coxae and to develop a tool specifically for Thai individuals. The total samples were 250 left os coxal dry bones divided into 200 samples (100 males, 100 females) for the training set and 50 samples for the test set. The age range was 26 – 94 years. We used the Suchey-Brooks method and Berg method for observing the pubic symphysis and the Buckberry-Chamberlain method for observing the auricular surface. Afterward we compared the dry bones and photo parts. Our results showed sex did not play a significant role in estimating the age-at-death. In both parts, the auricular surface yielded the highest accuracy (80 – 84%) with SEE = 13.99 – 14.24 years. The pubic symphysis showed an accuracy of 74 – 76% and SEE = 14.37 – 14.44 years. The results of the dry bone and photo parts did not differ significantly. In both dry bone and photo parts, the intra-observer agreement performed moderate to almost perfect agreement. On the other hand, the inter-observer agreement was slight to fair. In conclusion, our study can be potentially applied for distant consultation for age estimation using 2D pelvic images with a forensic anthropologist for estimating biological profiles

The Success of Serious Games and Gamified Systems in HIV Prevention and Care: Scoping Review

BackgroundAIDS, which is caused by HIV, has long been one of the most significant global public health issues. Since the beginning of the HIV epidemic, various types of nonelectronic communication tools have been commonly used in HIV/AIDS prevention and care, but studies that apply the potential of electronic games are still limited. ObjectiveWe aimed to identify, compare, and describe serious games and gamified systems currently used in HIV/AIDS prevention and care that were studied over a specific period of time. MethodsA scoping review was conducted into serious games and gamified systems used in HIV prevention and care in various well-known digital libraries from January 2010 to July 2021. ResultsAfter identifying research papers and completing the article selection process, 49 of the 496 publications met the inclusion criteria and were examined. A total of 32 articles described 22 different serious games, while 17 articles described 13 gamified systems for HIV prevention and care. ConclusionsMost of the studies described in the publications were conducted in the United States, while only a few studies were performed in sub-Saharan African countries, which have the highest global HIV/AIDS infection rates. Regarding the development platform, the vast majority of HIV/AIDS gaming systems were typically deployed on mobile devices. This study demonstrates the effectiveness of using serious games and gamified systems. Both can improve the efficacy of HIV/AIDS prevention strategies, particularly those that encourage behavior change

Heuristic neural network approach in histological sections detection of hydatidiform mole

A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k -fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets

Image analysis of histological features in molar pregnancies

Molar pregnancy (also known as hydatidiform mole, hydatid mole, gestational trophoblastic disease) represents forms of abnormal conception caused by defective fertilisation resulting in excess expression of paternal genes in placental tissue. There are two forms of hydatidiform mole: complete (diploid androgenetic) and partial (paternal triploid), the distinction between which is important for determining appropriate prognosis and management of patients. Both complete and partial hydatidiform moles are associated with increased risk of development of malignant gestational trophoblastic tumours, the risk being much greater for complete hydatidiform moles. Whilst in most cases the diagnosis of these moles can be reliably achieved on morphological histological assessment, these represent a continuing diagnostic problem for histopathologists since in early pregnancy complete hydatidiform moles, partial hydatidiform moles and non-molar hydropic miscarriages may be difficult to distinguish. In this paper, we propose a computational image analysis approach guided by the knowledge of expert pathologists in identifying essential distinguishing morphological criteria. The approach, which combines Fuzzy C-Means clustering with hue, saturation and value colour space, shows promising results as it is able to classify successfully the villi into appropriate regions, namely trophoblast and stroma, and extract areas of blood. However, because of the marked variations in size, shape and outline of the villi, and trophoblast proliferation, both within and between cases, the analysis shows that there is no single criteria which can reliably classify these products of conception and a combination of criteria is required