Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte

U-net and its variants for medical image segmentation: A review of theory and applications

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net

Blurry Boundary Delineation and Adversarial Confidence Learning for Medical Image Analysis

Low tissue contrast and fuzzy boundaries are major challenges in medical image segmentation which is a key step for various medical image analysis tasks. In particular, blurry boundary delineation is one of the most challenging problems due to low-contrast and even vanishing boundaries. Currently, encoder-decoder networks are widely adopted for medical image segmentation. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve more accurate segmentation performance. However, in many applications (e.g., images with blurry boundaries), these models often cannot precisely locate complex boundaries and segment tiny isolated parts. To solve this challenging problem, we empirically analyze why simple lateral connections in encoder-decoder architectures are not able to accurately locate indistinct boundaries. Based on the analysis, we argue learning high-resolution semantic information in the lateral connection can better delineate the blurry boundaries. Two methods have been proposed to achieve such a goal. a) A high-resolution pathway composed of dilated residual blocks has been adopted to replace the simple lateral connection for learning the high-resolution semantic features. b) A semantic-guided encoder feature learning strategy is further proposed to learn high-resolution semantic encoder features so that we can more accurately and efficiently locate the blurry boundaries. Besides, we also explore a contour constraint mechanism to model blurry boundary detection. Experimental results on real clinical datasets (infant brain MRI and pelvic organ datasets) show that our proposed methods can achieve state-of-the-art segmentation accuracy, especially for the blurry regions. Further analysis also indicates that our proposed network components indeed contribute to the performance gain. Experiments on an extra dataset also validate the generalization ability of our proposed methods. Generative adversarial networks (GANs) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is usually directly applied to the original supervised segmentation (synthesis) networks. The use of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot be improved as much as the qualitative performance, and it can even become worse in some cases. In this dissertation, I explore how adversarial learning could be more useful in supervised segmentation (synthesis) models, i.e., how to synchronously improve visual and quantitative performance. I first analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, an adversarial confidence learning framework is proposed for taking better advantage of adversarial learning; that is, besides the adversarial learning for emphasizing visual perception, the confidence information provided by the adversarial network is utilized to enhance the design of the supervised segmentation (synthesis) network. In particular, I propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. Furthermore, various loss functions of GANs are investigated and the binary cross entropy loss is finally chosen to train the proposed adversarial confidence learning system so that the modeling capacity of the discriminator is retained for confidence learning. With these settings, two machine learning algorithms are proposed to solve some specific medical image analysis problems. a) A difficulty-aware attention mechanism is proposed to properly handle hard samples or regions by taking structural information into consideration so that the irregular distribution of medical data could be appropriately dealt with. Experimental results on clinical and challenge datasets show that the proposed algorithm can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can synchronously improve the visual perception and quantitative performance. b) A semisupervised segmentation model is proposed to alleviate the everlasting challenge for medical image segmentation - lack of annotated data. The proposed method can automatically recognize well-segmented regions (instead of the entire sample) and dynamically include them to increase the label set during training. Specifically, based on the confidence map, a region-attention based semi-supervised learning strategy is designed to further train the segmentation network. Experimental results on real clinical datasets show that the proposed approach can achieve better segmentation performance with extra unannotated data.Doctor of Philosoph

Development of medical image/video segmentation via deep learning models

Image segmentation has a critical role in medical diagnosis systems as it is mostly the initial stage, and any error would be propagated in the subsequent analysis. Certain challenges, including Irregular border, low quality of images, small Region of Interest (RoI) and complex structures such as overlapping cells in images impede the improvement of medical image analysis. Deep learning-based algorithms have recently brought superior achievements in computer vision. However, there are limitations to their application in the medical domain including data scarcity, and lack of pretrained models on medical data. This research addresses the issues that hinder the progress of deep learning methods on medical data. Firstly, the effectiveness of transfer learning from a pretrained model with dissimilar data is investigated. The model is improved by integrating feature maps from the frequency domain into the spatial feature maps of Convolutional Neural Network (CNN). Training from scratch and the challenges ahead were explored as well. The proposed model shows higher performance compared to state-of-the-art methods by %2:2 and %17 in Jaccard index for tasks of lesion segmentation and dermoscopic feature segmentation respectively. Furthermore, the proposed model benefits from significant improvement for noisy images without preprocessing stage. Early stopping and drop out layers were considered to tackle the overfitting and network hyper-parameters such as different learning rate, weight initialization, kernel size, stride and normalization techniques were investigated to enhance learning performance. In order to expand the research into video segmentation, specifically left ventricular segmentation, U-net deep architecture was modified. The small RoI and confusion between overlapped organs are big challenges in MRI segmentation. The consistent motion of LV and the continuity of neighbor frames are important features that were used in the proposed architecture. High level features including optical flow and contourlet were used to add temporal information and the RoI module to the Unet model. The proposed model surpassed the results of original Unet model for LV segmentation by a %7 increment in Jaccard index

CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on the accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by accounting for the cross-slice relationship of adjacent slices, but current methods do not fully learn and exploit such relationships. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn the cross-slice relationship at different scales. The module can be utilized in any existing learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture the cross-slice information in prostate zonal segmentation and improve the performance of current state-of-the-art methods. Our method significantly improves segmentation accuracy in the peripheral zone, such that the segmentation results are consistent across all the prostate slices (apex, mid-gland, and base)

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Deep Multimodality Image-Guided System for Assisting Neurosurgery

Intrakranielle Hirntumoren gehören zu den zehn häufigsten bösartigen Krebsarten und sind für eine erhebliche Morbidität und Mortalität verantwortlich. Die größte histologische Kategorie der primären Hirntumoren sind die Gliome, die ein äußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen Hirnläsionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung für neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen Kontrastverstärkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und Anästhesie, was den Nutzen präopera-tiver Bilddaten für die Steuerung des Eingriffs einschränkt. Bildgesteuerte Systeme bieten Ärzten einen unschätzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner Bildgebungsmodalitäten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsächlich um computergestützte Systeme, die mit Hilfe von Computer-Vision-Methoden die Durchführung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen müssen jedoch immer noch den Operationsplan aus präoperativen Bildern gedanklich mit Echtzeitinformationen zusammenführen, während sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung überwachen. Daher war die Notwendigkeit einer Bildführung während neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems für die peri-operative bildgeführte Neurochirurgie (IGN), nämlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die Gesamtüberle-bensrate maximiert und die postoperative neurologische Morbidität minimiert wird. Im Rahmen dieser Arbeit werden zunächst neuartige Methoden für die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen präope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jüngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem für den Menschen verständliche, erklärbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die für die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zuständig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. Für das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework für die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den Würfelkoeffizienten für das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-Ansätze wie 3D-Faltungen über alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, für die Registrierung präoperativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgeführt: BITE und RESECT. Zwei erfahrene Neurochirurgen führten eine zusätzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung überlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. Darüber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfähige Ergebnisse liefern, was seine Allgemeingültigkeit unter Beweis stellt und daher für die intraoperative neurochirurgische Führung von Nutzen sein kann. Für das Modul "Erklärbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben Erklärungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklärbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. Darüber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-Modalitäten zur end-gültigen Vorhersage zu verstehen. Der Erklärungsprozess könnte medizinischen Fachleu-ten zusätzliche Informationen über die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display für die Eingriffsführung entwickelt, das die verfügbare kommerzielle Hardware wie iUS-Navigationsgeräte und Instrumentenverfolgungssysteme unterstützt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der Fähigkeit zur Integration von (1) präoperativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestätigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgeführt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jüngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen präzise zu führen und prä- und intraoperative Patientenbilddaten sowie interventionelle Geräte in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend für die Anwendung von Deep-Learning-Modellen zur Unterstützung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse

FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation

Solving variational image segmentation problems with hidden physics is often expensive and requires different algorithms and manually tunes model parameter. The deep learning methods based on the U-Net structure have obtained outstanding performances in many different medical image segmentation tasks, but designing such networks requires a lot of parameters and training data, not always available for practical problems. In this paper, inspired by traditional multi-phase convexity Mumford-Shah variational model and full approximation scheme (FAS) solving the nonlinear systems, we propose a novel variational-model-informed network (denoted as FAS-Unet) that exploits the model and algorithm priors to extract the multi-scale features. The proposed model-informed network integrates image data and mathematical models, and implements them through learning a few convolution kernels. Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features. Secondly, we further design a convolution block to fuse the extracted features from the previous stage, resulting in the final segmentation possibility. Experimental results on three different medical image segmentation tasks show that the proposed FAS-Unet is very competitive with other state-of-the-art methods in qualitative, quantitative and model complexity evaluations. Moreover, it may also be possible to train specialized network architectures that automatically satisfy some of the mathematical and physical laws in other image problems for better accuracy, faster training and improved generalization.The code is available at \url{https://github.com/zhuhui100/FASUNet}.Comment: 18 page