Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation

Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image analysis but have been performing poorly for cases when existing a significant imbalanced feature distribution, which is true for the airway data as the trachea and principal bronchi dominate most of the voxels whereas the lobar bronchi and distal segmental bronchi occupy a small proportion. In this paper, we propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to balance the training progress within-class distribution. Furthermore, a Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with superior sensitivity and minimize the variation of the distance map between the predictionand ground-truth. The proposed method is validated with the publically available reference airway segmentation datasets. The detected rate of branch and length on public EXACT'09 and BAS datasets are 82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the reliability and efficiency of the method in terms of improving the topology completeness of the segmentation performance while maintaining the overall topology accuracy.Comment: 10 page

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT

Die Entwicklung von Synchrotronlichtquellen der dritten Generation hat die Grundlage für die Untersuchung der 3D-Struktur opaker Proben mit einer Auflösung im Mikrometerbereich und höher geschaffen. Dies führte zur Entwicklung der Röntgen-Synchrotron-Mikro-Computertomographie, welche die Schaffung von Bildgebungseinrichtungen zur Untersuchung von Proben verschiedenster Art förderte, z.B. von Modellorganismen, um die Physiologie komplexer lebender Systeme besser zu verstehen. Die Entwicklung moderner Steuerungssysteme und Robotik ermöglichte die vollständige Automatisierung der Röntgenbildgebungsexperimente und die Kalibrierung der Parameter des Versuchsaufbaus während des Betriebs. Die Weiterentwicklung der digitalen Detektorsysteme führte zu Verbesserungen der Auflösung, des Dynamikbereichs, der Empfindlichkeit und anderer wesentlicher Eigenschaften. Diese Verbesserungen führten zu einer beträchtlichen Steigerung des Durchsatzes des Bildgebungsprozesses, aber auf der anderen Seite begannen die Experimente eine wesentlich größere Datenmenge von bis zu Dutzenden von Terabyte zu generieren, welche anschließend manuell verarbeitet wurden. Somit ebneten diese technischen Fortschritte den Weg für die Durchführung effizienterer Hochdurchsatzexperimente zur Untersuchung einer großen Anzahl von Proben, welche Datensätze von besserer Qualität produzierten. In der wissenschaftlichen Gemeinschaft besteht daher ein hoher Bedarf an einem effizienten, automatisierten Workflow für die Röntgendatenanalyse, welcher eine solche Datenlast bewältigen und wertvolle Erkenntnisse für die Fachexperten liefern kann. Die bestehenden Lösungen für einen solchen Workflow sind nicht direkt auf Hochdurchsatzexperimente anwendbar, da sie für Ad-hoc-Szenarien im Bereich der medizinischen Bildgebung entwickelt wurden. Daher sind sie nicht für Hochdurchsatzdatenströme optimiert und auch nicht in der Lage, die hierarchische Beschaffenheit von Proben zu nutzen. Die wichtigsten Beiträge der vorliegenden Arbeit sind ein neuer automatisierter Analyse-Workflow, der für die effiziente Verarbeitung heterogener Röntgendatensätze hierarchischer Natur geeignet ist. Der entwickelte Workflow basiert auf verbesserten Methoden zur Datenvorverarbeitung, Registrierung, Lokalisierung und Segmentierung. Jede Phase eines Arbeitsablaufs, die eine Trainingsphase beinhaltet, kann automatisch feinabgestimmt werden, um die besten Hyperparameter für den spezifischen Datensatz zu finden. Für die Analyse von Faserstrukturen in Proben wurde eine neue, hochgradig parallelisierbare 3D-Orientierungsanalysemethode entwickelt, die auf einem neuartigen Konzept der emittierenden Strahlen basiert und eine präzisere morphologische Analyse ermöglicht. Alle entwickelten Methoden wurden gründlich an synthetischen Datensätzen validiert, um ihre Anwendbarkeit unter verschiedenen Abbildungsbedingungen quantitativ zu bewerten. Es wurde gezeigt, dass der Workflow in der Lage ist, eine Reihe von Datensätzen ähnlicher Art zu verarbeiten. Darüber hinaus werden die effizienten CPU/GPU-Implementierungen des entwickelten Workflows und der Methoden vorgestellt und der Gemeinschaft als Module für die Sprache Python zur Verfügung gestellt. Der entwickelte automatisierte Analyse-Workflow wurde erfolgreich für Mikro-CT-Datensätze angewandt, die in Hochdurchsatzröntgenexperimenten im Bereich der Entwicklungsbiologie und Materialwissenschaft gewonnen wurden. Insbesondere wurde dieser Arbeitsablauf für die Analyse der Medaka-Fisch-Datensätze angewandt, was eine automatisierte Segmentierung und anschließende morphologische Analyse von Gehirn, Leber, Kopfnephronen und Herz ermöglichte. Darüber hinaus wurde die entwickelte Methode der 3D-Orientierungsanalyse bei der morphologischen Analyse von Polymergerüst-Datensätzen eingesetzt, um einen Herstellungsprozess in Richtung wünschenswerter Eigenschaften zu lenken

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

6. Uluslararası Öğrenciler Fen Bilimleri Kongresi: Tam Metin Bildiriler Kitabı, 20-21 Mayıs 2022

Çevrimiçi (X, 434 Sayfa; 26 cm.)Değerli Katılımcılar, Meslektaşlarım ve Uluslararası Öğrenciler, 6. Uluslararası Öğrenciler Fen Bilimleri Kongresi Tam metin Kitabını etkinliğin yazarlarına ve katılımcılarına sunmak bizler için büyük bir onur ve ayrıcalıktır. Bunu yararlı, heyecan ve ilham verici bulacağınızı umuyoruz. Son beş yıldır çeşitli bilim dallarında çalışan genç uluslararası araştırmacıları bir araya getirmek amacıyla kongrelerimizi düzenledik ve bu hepimizi gerçekten motive etti. Küresel Covid-19 pandemisinin ardından altıncı kongreyi, yüz yüze canlı ve çevrimiçi sanal oturumları birleştirerek karma bir etkinlik olarak düzenledik. Kongrenin ilk günü olan 20 Mayıs’ta, 100'den fazla katılımcıyı bir araya getiren ve tamamen yüz yüze sekiz oturum gerçekleştirildi. Bu ilk günün sabahında davetli konuşmacılarımız tarafından iki ilgi çekici sunum yapıldı: Ege Üniversitesi'nden Prof. Dr. Bahattin Tanyolaç “Covid-19 Aşıları” ve Gebze Teknik Üniversitesi'nden Dr. Yakup Genç “Metaverse” hakkında konuştular. Etkinliğin ikinci gününde dokuz çevrimiçi oturum Zoom üzerinden gerçekleştirildi ve YouTube üzerinden canlı olarak yayınlandı; bu oturumların videolarına Youtube kanalımızdan ulaşabilirsiniz. Altıncı kongremizi de yine büyük bir istek ve heyecanla gerçekleştirdik. İki gün süren kongrede, yirmi sekiz farklı ülkeden yüz elliyi aşkın genç araştırmacı ve akademisyen bir araya geldi ve on yedi oturumda toplam doksan yedi bildiri sunuldu. Bildirilerin kırk yedi tanesi canlı yüz yüze, elli tanesi ise çevrimiçi olarak sunuldu. Öte yandan, elli iki bildiri uluslararası (Türk olmayan) katılımcılar tarafından, kırk beş bildiri ise Türk katılımcılar tarafından sunuldu. Kongre, özellikle fen bilimleri alanında eğitimlerine devam eden uluslararası öğrencilerin ve genç akademisyenlerin önlerindeki akademik camia ile etkileşimlerini gayet samimi bir ortam sunarak teşvik ederken, yeni ve güncel çalışmalarını sunmaları ve tartışmaları için de güzel bir fırsat sağlamış oldu. Onların katkıları sayesinde Kongre olabildiğince seçkin ve nitelikli bir düzeye ulaşmış oldu. Kongre, Ziraat Mühendisliği, Mimarlık, Biyoloji ve Biyomühendislik, Kimya ve Kimya Mühendisliği, İnşaat Mühendisliği, Bilgisayar Bilimi ve Mühendisliği, Elektrik, Elektronik ve Haberleşme Mühendisliği, Enerji, Gıda Mühendisliği, Jeoloji Mühendisliği, Makine Mühendisliği, Matematik, Malzeme Bilimi, Metalürji ve Malzeme Mühendisliği, Mekatronik Mühendisliği, Nanoteknoloji, Fizik, Tekstil Mühendisliği, Kentsel ve Bölgesel Planlama, vb. çok çeşitli konulardaki son gelişmeleri tartışmak için keyifli bir ortam sağladı. Tüm katılımcılara kongre programımıza ve dolayısıyla tam metin kitabımıza yaptıkları katkılardan dolayı teşekkür ederiz. Ayrıca verdikleri destek ile bu kongrenin gerçekleşmesine katkı sağlayan İzmir Kâtip Çelebi Üniversitesi’ne, Uluslararası Öğrenci Dernekleri Federasyonu’na (UDEF), Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’na (TÜBİTAK) ve ana organizatörümüz İzmir Uluslararası Misafir Öğrenci Derneği'ne teşekkürlerimizi arz ederiz. Organizasyon komitemize ve etkinlik süresince gönüllü olarak çalışan tüm öğrencilere içten şükran ve takdirlerimi sunuyorum. Bu kongre dizisinin devam eden başarısı, 2023'te düzenlenmeyi hedeflediğimiz 7. Uluslararası Öğrenciler Fen Bilimleri Kongresi için planlamanın artık güvenle ilerleyebileceği anlamına geliyor; bu kongremiz de muhtemelen hem çevrimiçi hem de yüz yüze olacak. Katkılarından dolayı tüm yazarlara, katılımcılara ve gönüllülere teşekkür ederiz. Prof. Dr. Mehmet Çevik Kongre Başkan