Content-Based Microscopic Image Retrieval System for Multi-Image Queries

Akakın, Hatice Çınar; Gürcan, Metin N.

doi:10.1109/titb.2012.2185829

Cited by 103 publications

(52 citation statements)

References 40 publications

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“…The need for more accurate and faster analyses of these images led to the development of the fields of digital pathology and radiomics, which aim for high-throughput automatic evaluation of clinical imaging, such as histopathology, microscopy, MRI, computed tomography (CT) or positron emission tomography (PET). Current advances include protocols to reconstruct the spatial 3D architectures of DCIS from 2D histology images stained with haematoxylin and eosin (H&E) [139] and multi-tiered content-based image algorithms for classification of tumour grades [140]. Histology samples and digital microscopy analyses were also used to evaluate the differences between distinct regions of interest within the same tumour [141].…”

Section: Medical Imagingmentioning

confidence: 99%

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Karolak

Markov

McCawley

et al. 2018

J. R. Soc. Interface.

114

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A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multicomponent tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

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Section: Medical Imagingmentioning

confidence: 99%

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Karolak

Markov

McCawley

et al. 2018

J. R. Soc. Interface.

114

View full text Add to dashboard Cite

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“…the so-called 'Hot Spots'; a term describing the most active areas of cancer proliferation [45]. Making a pathological diagnosis is further enhanced by the ability to search and compare results with images from digital databases, however this way so far has not been adopted for routine diagnostics [46].…”

Section: Digital Diagnosticsmentioning

confidence: 99%

Future perspectives of digital pathology

Prochorec‐Sobieszek

2016

Nowotwory. Journal of Oncology

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Technological advances have enabled innovative solutions to be achieved in pathology based on digital imaging, now superseding those of conventional microscopy. Digital pathology has been defined as 'virtual microscopy' and depends on computer-generated digital imaging of microscope slides (WSI -whole slide imaging) which are in turn created, reviewed, managed, shared, analysed and interpreted. Such WSI systems and digital consulting platforms are now used for teaching, scientific research, telepathology / teleconsultation and diagnostics. They also permit easy and interactive sharing of WSI that can be integrated into other medical information systems. The software for automated image analysis and computer aided diagnosis can thereby make highly accurate diagnoses and help standardise study findings. Despite the technique's many advantages, its noted drawbacks include high equipment and software costs, image quality issues of standardisation and most importantly, that pathologists are reluctant to use it routinely for making diagnoses. NOWOTWORY J Oncol 2016; 66, 4: 277-285

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“…A recent study [157] proposed the use of multiple query images to augment the retrieval process. These images were of the same modality: microscopic images of cells.…”

Section: Multiple Images and Modalitiesmentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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Content-Based Microscopic Image Retrieval System for Multi-Image Queries

Cited by 103 publications

References 40 publications

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

Future perspectives of digital pathology

A graph-based approach for the retrieval of multi-modality medical images

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