Анализ подходов к глубокому обучению для автоматизированного выделения и сегментации предстательной железы: обзор литературы

Background. Delineation of the prostate boundaries represents the initial step in understanding the state of the whole organ and is mainly manually performed, which takes a long time and directly depends on the experience of the radiologists. Automated prostate selection can be carried out by various approaches, including using artificial intelligence and its subdisciplines – machine and deep learning.Aim. To reveal the most accurate deep learning-based methods for prostate segmentation on multiparametric magnetic resonance images.Materials and methods. The search was conducted in July 2022 in the PubMed database with a special clinical query (((AI) OR (machine learning)) OR (deep learning)) AND (prostate) AND (MRI). The inclusion criteria were availability of the full article, publication date no more than five years prior to the time of the search, availability of a quantitative assessment of the reconstruction accuracy by the Dice similarity coefficient (DSC) calculation.Results. The search returned 521 articles, but only 24 papers including descriptions of 33 different deep learning networks for prostate segmentation were selected for the final review. The median number of cases included for artificial intelligence training was 100 with a range from 25 to 365. The optimal DSC value threshold (0.9), in which automated segmentation is only slightly inferior to manual delineation, was achieved in 21 studies.Conclusion. Despite significant achievements in the development of deep learning-based prostate segmentation algorithms, there are still problems and limitations that should be resolved before artificial intelligence can be implemented in clinical practice.Введение. Определение границ предстательной железы является начальным шагом в понимании состояния органа и в основном выполняется вручную, что занимает длительное время и напрямую зависит от опыта рентгенолога. Автоматизация в выделении предстательной железы может быть осуществлена различными подходами, в том числе с помощью искусственного интеллекта и его субдисциплин – машинного и глубокого обучения.Цель работы – детальный анализ литературы для определения наиболее эффективных способов автоматизированной сегментации предстательной железы по снимкам мультипараметрической магнитно-резонансной томографии посредством глубокого обучения.Материалы и методы. Поиск публикаций проводился в июле 2022 г. в поисковой системе PubMed с помощью клинического запроса (((AI) OR (machine learning)) OR (deep learning)) AND (prostate) AND (MRI). Критериями включения были доступность полного текста статьи, дата публикации не более 5 лет на момент поиска, наличие количественной оценки точности реконструкции предстательной железы с помощью коэффициента Серенсена–Дайса (Dice similarity coefficient, DSC).Результаты. В результате поиска найдена 521 статья, из которой в анализ были включены только 24 работы, содержавшие описание 33 различных способов глубокого обучения для сегментации предстательной железы. Медиана количества исследований, включенных для обучения искусственного интеллекта, составила 100 с диапазоном от 25 до 365. Оптимальным значением DSC, при котором автоматизированная сегментация лишь незначительно уступает ручному послойному выделению предстательной железы, составляет 0,9. Так, DSC выше порогового достигнут в описании 21 алгоритма.Заключение. Несмотря на значимые достижения в автоматизированной сегментации предстательной железы с помощью алгоритмов глубокого обучения, до сих пор существует ряд проблем и ограничений, требующих решения для внедрения искусственного интеллекта в клиническую практику

Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools—DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. The network successfully detects the prostate region and segments the gland and its zones. Compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of 89.5±2% vs. 89.1±2.2 %, and for the peripheral zone of 78.1± 2.5% vs. 75±3%. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones

AI based segmentation of the prostate

Magnetic resonance imaging (MRI) provides increasingly reliable imaging of prostate cancer (PCa) and can improve the detection of lesions and the performance of targeted biopsies. In this regard, segmentation of the prostate in the MRI dataset is critical for several tasks, including the creation of three-dimensional models, e.g., for navigational purposes when planning biopsies or interventional therapies, for planning radiotherapy, for improved volume estimation to assess disease progression, and for automated detection of prostate zones and PCa. However, segmentation by hand is very time-consuming, making an automated machine-based solution desirable. Methods: For this project, a data set of 158 MRI examinations of the prostate was compiled, which meet the technical requirements of the PIRADS V2.1 standard. These included 102 patients with histologically confirmed prostate carcinoma and an image morphological finding of PIRADS 4 or higher. The examinations were then divided into a training data set and a test data set. Both datasets were manually segmented by two subject matter experts with several years of experience in uroradiological imaging, firstly annotating the anatomy and zonal divisions and secondly annotating the tumor regions. Furthermore, a deep learning model was developed and trained on the segmentation of anatomy and tumor region using the training dataset. Subsequently, the agreement of the segmentations of the experts among themselves and the agreement of the segmentations of the model with those of the experts were compared on the test data set. Results: The agreement between the segmentations of the two experts was highest for the central zone, followed by the peripheral zone and lowest for the tumor region. A similar picture was seen for the segmentations of the model. There was no significant difference in the agreement between the model and the respective experts 1 and 2. However, a worse agreement between the model to the experts compared to the interrater agreement between the experts could be observed. Conclusion: Although the deep learning model used for this Thesis for prostate anatomy segmentation and tumor region detection and segmentation could not quite reach the human expert standard, a perspective and great potential for further research and progress in this area of medical image analysis can still be seen. Automated segmentations and tumor detection may facilitate and accelerate clinical workflow and improve future diagnostics and therapies. In the context of further technical advances, a similar quality and safety as long-time trained human experts can be expected.Die Magnetresonanztomographie (MRT) ermöglicht eine zuverlässige Darstellung von Prostatakrebs (PCa) und kann die Erkennung von Läsionen und die Durchführung gezielter Biopsie verbessern. Die Segmentierung der Prostata im MRT Datensatz ist dabei für viele Aufgaben von entscheidender Bedeutung, u. a. für die Erstellung dreidimensionaler Modelle, z. B. zu Navigationszwecken bei der Planung von Biopsien oder interventionellen Therapien, für die Planung einer Strahlentherapie, für eine verbesserte Volumenschätzung zur Beurteilung des Krankheitsverlaufs und für die automatisierte Erkennung der Anatomie und von PCa. Eine Segmentierung von Hand ist jedoch zeitaufwändig, weshalb eine automatisierte maschinelle Lösung erstrebenswert ist. Methoden Es wurde ein Datensatz von insgesamt 158 MRT Untersuchungen der Prostata zusammengestellt, welche den technischen Anforderungen des PI-RADS V2.1 Standards entsprechen. Hierunter befanden sich 102 Patienten mit histologisch gesicherten Prostatakarzinomen und einem bildmoprhologischen Befund von PI-RADS 4 oder höher. Die Untersuchungen wurden daraufhin auf einen Trainingsdatensatz und einen Testdatensatz aufgeteilt. Beide Datensätze wurden händisch durch zwei Experten mit mehrjähriger Erfahrung in uroradiologischer Bildgebung segmentiert, wobei zum einen die zonale Anatomie und zum anderen die Tumorregionen annotiert wurden. Des Weiteren wurde ein Deep Learning Modell entwickelt und mit Hilfe des Trainingsdatensatzes auf die Segmentierung der Anatomie und der Tumorregion trainiert. Anschließend wurde am Testdatensatz die Übereinstimmung der Segmentierungen der Experten untereinander sowie die Übereinstimmung der Segmentierungen des Modells mit denen der Experten verglichen. Ergebnisse Die Übereinstimmung zwischen den Segmentierungen der beiden Experten war am höchsten für die zentrale Drüse, gefolgt von der peripheren Zone und am niedrigsten für die Tumorregion. Ein ähnliches Bild zeigte sich auch für die Segmentierungen desModells. Es bestand kein signifikanter Unterschied in der Übereinstimmung zwischen dem Modell und den jeweiligen Experten 1 und 2. Es konnte jedoch eine schlechtere Übereinstimmung zwischen dem Modell zu den Experten gegenüber der Interrater Übereinstimmung zwischen den Experten festgestellt werden. Schlussfolgerung Obgleich das verwendete Deep Learning Modells für die Segmentierung der Prostataanatomie sowie der Segmentierung der Tumorregion nicht ganz den menschlichen Expertenstandard erreichen konnte, lässt sich dennoch eine Perspektive und großes Potential für weitere Forschung und Fortschritte in diesem Bereich der medizinischen Bildanalyse erkennen. Automatisierte Segmentierungen und Tumordetektionen können den klinischen Arbeitsfluss erleichtern und beschleunigen sowie zukünftige Diagnostik und Therapien verbessern. Im Rahmen weiterer technischer Fortschritte ist eine ähnliche Qualität und Sicherheit wie langjährig antrainierte menschliche Experten erwartbar

A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning

In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. However, there are a number of deficiencies associated with this approach. Firstly, in a real-life scenario, the medical team considers multiple factors rather than just the similarity between two cases and not always the most similar case provides with the most appropriate solution. Thus, in this thesis, the cases with high similarity to a new case have been evaluated with the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach takes into account multiple criteria besides similarity to prescribe a final solution. Moreover, the obtained dose plans were optimised through a Goal Programming mathematical model to improve the results. By incorporating oncologists’ experiences about violating the conventionally available dose limits a system was devised to manage the trade-off between treatment risk for sensitive organs and necessary actions to effectively eradicate cancer cells. Additionally, the success rate of the treatment, the 2-years cancer free possibility, has a vital role in the efficiency of the prescribed solutions. To consider the success rate, as well as uncertainty involved in human judgment about the values of different features of radiotherapy Data Envelopment Analysis (DEA) based on grey numbers, was used to assess the efficiency of different treatment plans on an input and output based approach. In order to deal with limitations involved in DEA regarding the number of inputs and outputs, we presented an approach for Factor Analysis based on Principal Components to utilize the grey numbers. Finally, to improve the CBR base of the system, we applied Grey Relational Analysis and Gaussian distant based CBR along with features weight selection through Genetic Algorithm to better handle the non-linearity exists within the problem features and the high number of features. Finally, the efficiency of each system has been validated through leave-one-out strategy and the real dataset. The results demonstrated the efficiency of the proposed approaches and capability of the system to assist the medical planning team. Furthermore, the integrated approaches developed within this thesis can be also applied to solve other real-life problems in various domains other than healthcare such as supply chain management, manufacturing, business success prediction and performance evaluation