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Wpływ wstępnego schłodzenia ciała z wykorzystaniem automatycznego nawiewu na wartość diagnostyczną obrazów termicznych gruczołu piersiowego

Szurko Agnieszka, Kiełboń Agnieszka, Lasyk Dorota, Kokoszka Katarzyna, Stanek Agata, Cholewka Armand

Inżynier i Fizyk Medyczny

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2022

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Vol. 11, nr 6

473--478

PL

Termografia, jako nieinwazyjna metoda pozwalająca szybko i skutecznie wykryć obszary o podwyższonej temperaturze powierzchni ciała, idealnie nadaje się do celów wspomagających diagnostykę schorzeń piersi. Celem przeprowadzonych badań było udoskonalenie procedur badawczych z wykorzystaniem termowizji poprzez zastosowanie wstępnego schłodzenia ciała ochotników. Pomiary wykonano w Laboratorium Medycyny Sportowej Instytutu Inżynierii Biomedycznej na Uniwersytecie Śląskim. Grupa badawcza składała się z 5 zdrowych, młodych osób. Ciało ochotników zostało schłodzone w komorze kriogenicznej CrioSpace firmy JBG2 przy pomocy powietrza o temperaturze 0°C. Na uzyskanych termogramach gruczołów piersiowych oznaczone zostały obszary pomiarowe według stosowanej w medycynie konwencji. Analizowane termogramy oraz parametry temperaturowe jednoznacznie wykazały zwiększenie zakresu obserwowanych różnic temperaturowych po schłodzeniu piersi. Kontrast temperaturowy obliczany na podstawie różnic średnich temperatur symetrycznych obszarów piersi wzrastał nawet kilkakrotnie. Prosty zabieg ochłodzenia badanego obszaru może zatem podnieść czułość i dokładność pomiarów termowizyjnych.

EN

Thermography as a non-invasive method that allows to detect quickly and effectively areas with increased body surface temperature, is ideally suited for supporting the diagnosis of breast diseases. The aim of the research was to improve research procedures using thermovision by applying pre-cooling of the volunteers’ bodies. The measurements were made at the Sports Medicine Laboratory of the Institute of Biomedical Engineering at the University of Silesia. The research group consisted of 5 healthy young people. The body of the volunteers was cooled in the CryoSpace cryogenic chamber by JBG2, with the use of air at a temperature of 0℃. Measurement areas were marked on the obtained thermographs of the mammary glands according to the convention used in medicine. The analysed thermograms and temperature parameters clearly showed an increase in the range of observed temperature differences after breast cooling. The temperature contrast, calculated on the basis of differences in mean temperatures of symmetrical breast areas, increased even several times. A simple treatment of cooling the examined area can therefore increase the sensitivity and accuracy of thermal imaging measurements.

2

Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization

Thawkar Shankar

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 4

1094--1111

EN

The purpose of this study is to develop a hybrid algorithm for feature selection and classification of masses in digital mammograms based on the Crow search algorithm (CSA) and Harris hawks optimization (HHO). The proposed CSAHHO algorithm finds the best features depending on their fitness value, which is determined by an artificial neural network. Using an artificial neural network and support vector machine classifiers, the best features determined by CSAHHO are utilized to classify masses in mammograms as benign or malignant. The performance of the suggested method is assessed using 651 mammograms. Experimental findings show that the proposed CSAHHO tends to be the best as compared to the original CSA and HHO algorithms when evaluated using ANN. It achieves an accuracy of 97.85% with a kappa value of 0.9569 and area under curve AZ = 0.982 ± 0.006. Furthermore, benchmark datasets are used to test the feasibility of the suggested approach and then compared with four state-of-the-art algorithms. The findings indicate that CSAHHO achieves high performance with the least amount of features and support to enhance breast cancer diagnosis.

3

Zastosowanie trastuzumabu w leczeniu raka piersi u pacjentów z nadekspresją receptora HER2

Jaźwiec Weronika, Jędroszka Grzegorz, Rychlewska Martyna

Inżynier i Fizyk Medyczny

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2021

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Vol. 10, nr 3

261--264

PL

Nowotwór piersi to najgroźniejszy złośliwy nowotwór występujący wśród kobiet w Polsce. Znajduje się na pierwszym miejscu zachorowań na nowotwory wśród kobiet, a także jest jedną z najczęstszych przyczyn zgonów z powodu nowotworów u kobiet. Pacjentki z inwazyjną postacią raka, u których potwierdzono nadekspresję białka receptorowego HER2, przechodzą wyjątkowo agresywny przebieg choroby. W artykule przedstawiono proces leczenia kobiet przy zastosowaniu przeciwciała monoklonalnego – trastuzumabu, dzięki któremu odnotowano znaczące korzyści w trakcie leczenia prowadzące do degradacji HER2. Należy zaznaczyć, że artykuł nie odzwierciedla w pełni złożoności tematu, ale daje spojrzenie na współczesne zastosowanie przeciwciała monoklonalnego w terapii, co pozwala na całkowitą lub częściową regresję choroby nowotworowej oraz zwiększenie czasu przeżycia pacjentek z nadekspresją receptora HER2.

EN

Breast canceris the most dangerous malignant tumor occurring among women in Poland. It is in the first place of cancercases and also, is one of the most common causes of cancer deaths among women. Patients with invasive form of cancer in whom the expression of HER2 receptor protein has been confirmed, under go extremely aggressive course of the disease. There fore this articleis a treatment presentation including monoclonal antibody – trastuzumab, thanks to which significant benefits during treatment leading to HER2 degradation have been reported. It should be noted that this article does not fully reflect the complexity of the topic, but gives a look at the contemporary use of monoclonal antibody for therapy, which allowsus to complete or partially regress the cancer and increase the survival of patients with HER2 over expression.

4

Breast cancer nuclei segmentation and classification based on a deep learning approach

Kowal Marek, Skobel Marcin, Gramacki Artur, Korbicz Józef

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 1

85--106

EN

One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy without aspiration. Cell nuclei are the most important elements of cancer diagnostics based on cytological images. Therefore, the first step of successful classification of cytological images is effective automatic segmentation of cell nuclei. The aims of our study include (a) development of segmentation methods of cell nuclei based on deep learning techniques, (b) extraction of some morphometric, colorimetric and textural features of individual segmented nuclei, (c) based on the extracted features, construction of effective classifiers for detecting malignant or benign cases. The segmentation methods used in this paper are based on (a) fully convolutional neural networks and (b) the marker-controlled watershed algorithm. For the classification task, seven various classification methods are used. Cell nuclei segmentation achieves 90% accuracy for benign and 86% for malignant nuclei according to the F-score. The maximum accuracy of the classification reached 80.2% to 92.4%, depending on the type (malignant or benign) of cell nuclei. The classification of tumors based on cytological images is an extremely challenging task. However, the obtained results are promising, and it is possible to state that automatic diagnostic methods are competitive to manual ones.

5

Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

Yan Yutong, Conze Pierre-Henri, Quellec Gwenolé, Lamard Mathieu, Cochener Beatrice, Coatrieux Gouenou

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

746--757

EN

Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the localization of suspicious regions of interest followed by segmentation, towards further lesion classification into benign versus malignant. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. However, manually segmenting breast masses from native mammograms is time-consuming and error-prone. Therefore, an integrated computer-aided diagnosis system is required to assist clinicians for automatic and precise breast mass delineation. In this work, we present a two-stage multiscale pipeline that provides accurate mass contours from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is employed to fine-delineate candidate masses. Unlike most previous studies based on segmentation from regions, our framework handles mass segmentation from native full mammograms without any user intervention. Trained on INbreast and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of 80.44% on INbreast test images, outperforming state-of-the-art. Our system shows promising accuracy as an automatic full-image mass segmentation system. Extensive experiments reveals robustness against the diversity of size, shape and appearance of breast masses, towards better interaction-free computer-aided diagnosis.

6

Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

Pawar Shivaji D., Sharma Kamal Kr., Sapate Suhas G., Yadav Geetanjali Y.

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1224--1241

EN

Digital mammography acts as a unique screening technology to protect the lives of females against breast cancer for the past few decades. Mammographic breast density is a well-known biomarker and plays a substantial role in breast cancer prediction and treatments. Breast density is calculated based on the opacity of fibro-glandular tissue reflected on digital mammograms concerning the whole area of the breast. The opacity of pectoral muscle and fibro-glandular tissue is similar to each other; hence, the small presence of the pectoral muscle in the breast area can hamper the accuracy of breast density classification. Successful removal of pectoral muscle is challenging due to changes in shape, size, and texture of pectoral muscle in every MLO and LMO views of mammogram. In this article, the depth-first search (DFS) algorithm is proposed to remove artifacts and pectoral muscle from digital mammograms. In the proposed algorithm, image enhancement is performed to improve the pixel quality of the input image. The whole breast as a single connected component is identified from the background region to remove the artifacts and tags. The depth-first search method with and without the heuristic approach is used to delineate the pectoral muscle, and then final suppression is performed on it. This algorithm is tested on 2675 images of the DDSM dataset, which is further divided into four density classes as per BIRADs classification. Segmentation results are calculated individually on each BIRADs density class of the DDSM dataset. Results are validated subjectively by the expert’s Radiologist’s ground truth with segmentation accuracy and objectively by the Jaccard coefficient and a dice similarity coefficient. This algorithm is found robust on each density class and provides overall segmentation accuracy of 86.18%, a mean value of Jaccard index, and a Dice similarity coefficient of 0.9315 and 0.9548, respectively. The experimental results show that the proposed algorithms applied for pectoral muscle removal follow the ground truth marked by an expert radiologist. The proposed algorithm can be part of the pre-processing unit of breast density measurement and breast cancer detection system used during clinical practice.

7

Computational methods for automated mitosis detection in histopathology images: A review

Mathew Tojo, Kini Jyoti R., Rajan Jeny

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

64--82

EN

Mitosis detection is an important step in pathology procedures in the context of cancer diagnosis and prognosis. Prevalent process for this task is by manually observing Hematox-ylin and Eosin (H & E) stained histopathology sections on glass slides through a microscope by trained pathologists. This conventional approach is tedious, error-prone, and has shown high inter-observer variability. With the advancement of computational technologies, automating mitosis detection by the use of image processing algorithms has attracted significant research interest. In the past decade, several methods appeared in the literature, addressing this problem and they have shown encouraging incremental progress towards a clinically usable solution. Mitosis count is an important parameter in grading of breast cancer and glioma, unlike other cancer types. Driven by the availability of multiple public datasets and open contests, most of the methods in literature address mitosis detection in breast cancer images. This paper is a comprehensive review of the methods published in the area of automated mitotic cell detection in H & E stained histopathology images of breast cancer in the last 10 years. We also discuss the current trends and future prospects of this clinically relevant task, augmenting humanity's fight against cancer.

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Zastosowanie obrazowania termicznego w ocenie efektów radioterapii u pacjentek po mastektomii

Plaza Dominika, Baic Agnieszka, Lange Barbara, Stanek Agata, Szurko Agnieszka, Sieroń Karolina, Ślosarek Krzysztof, Cholewka Armand

Inżynier i Fizyk Medyczny

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2020

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Vol. 9, nr 4

277--280

PL

W niniejszej pracy skupiono się na termicznej ocenie efektów radioterapii u pacjentek po mastektomii. Otrzymane wyniki pokazały zarówno istotną różnicę w temperaturze obszaru leczonego i zdrowej piersi, jak i odpowiedź termiczną piersi nienaświetlanej, wskazując na wzrost temperatury tkanek zdrowych. Ponadto widać rozbieżności w odpowiedzi termicznej u pacjentek, co może być związane zarówno z indywidualną odpowiedzią organizmu na zastosowaną dawkę promieniowania, jak i w przypadku pacjentek po zakończeniu leczenia wskazywać na uruchomienie procesów regeneracyjnych tkanek.

EN

The main goal of the study was thermal evaluation of radiotherapy effects in patients after mastectomy. Obtained results showed significant differences between body temperature irradiated and the healthy one. Moreover, some temperature rise of healthy non-irradiated area has been obtained. There were also differences in thermal response between patients what may be explained by time of imaging – during therapy it will be connected strictly with irradiation dose absorbed but after therapy with tissue regeneration processed occurred after therapy.

9

Zastosowanie obrazowania termicznego w diagnostyce nowotworów piersi

Brzezińska D., Baic A., Cholewka A., Stankiewicz M., Ślosarek K., Bzowski J., Stanek A., Sieroń K.

Inżynier i Fizyk Medyczny

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2018

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Vol. 7, nr 5

345--349

PL

Przyczyną dużej śmiertelności pacjentek chorych na nowotwory piersi jest zbyt późna wykrywalność zmian nowotworowych. Konieczność wprowadzenia bezpiecznej i bezinwazyjnej metodyki stanowi obecnie wyzwanie badawcze. Jedną z metod, która może wspomóc diagnostykę nowotworów piersi, jest termografia w podczerwieni. Znalazła ona szerokie zastosowanie w wielu dziedzinach przemysłu, jak również z powodzeniem jest stosowana w medycynie. Kamery termowizyjne wraz ze specjalistycznym oprogramowaniem umożliwiają analizę obrazów termograficznych różnych schorzeń, w których zmiana zlokalizowana jest na powierzchni skóry lub pod jej powierzchnią, co stanowi zaletę, ale również i wadę tej techniki diagnostycznej. Niniejsza praca ma na celu przeprowadzenie badań, które pozwolą ocenić przydatność obrazowania termograficznego w diagnostyce, jak i efektach leczenia nowotworów piersi z wykorzystaniem radioterapii.

EN

Currently, a large percentage of women’s deaths are breast cancers as opposed to other disease entities The reason for this is that the detectability of cancerous changes is too late Therefore, the current research challenge is the need to develop a new, safe and non-invasive methodology. Infrared thermography is one such method that can help diagnose the breast cancer It is a method that has successfully found application in many fields of science, including medicine. Thermal imaging cameras with specialized software allow for the analysis of thermographic images of diseases in which the lesion is located on the surface of the skin or just below the surface of the skin. Based on the temperature gradient, you can locate and assess the affected area. This work aims to conduct studies that will assess the usefulness of thermography imaging in diagnostics and to observe the effect of radiotherapy treatment of breast cancer.

10

Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images

Alirezazadeh P., Hejrati B., Monsef-Esfahani A., Fathi A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

671--683

EN

Breast cancer has high incidence rate compared to the other cancers among women. This disease leads to die if it does not diagnosis early. Fortunately, by means of modern imaging procedure such as MRI, mammography, thermography, etc., and computer systems, it is possible to diagnose all kind of breast cancers in a short time. One type of BC images is histology images. They are obtained from the entire cut-off texture by use of digital cameras and contain invaluable information to diagnose malignant and benign lesions. Recently by requesting to use the digital workflow in surgical pathology, the diagnosis based on whole slide microscopy image analysis has attracted the attention of many researchers in medical image processing. Computer aided diagnosis (CAD) systems are developed to help pathologist make a better decision. There are some weaknesses in histology images based CAD systems in compared with radiology images based CAD systems. As these images are collected in different laboratory stages and from different samples, they have different distributions leading to mismatch of training (source) domain and test (target) domain. On the other hand, there is the great similarity between images of benign tumors with those of malignant. So if these images are analyzed undiscriminating, this leads to decrease classifier performance and recognition rate. In this research, a new representation learning-based unsupervised domain adaptation method is proposed to overcome these problems. This method attempts to distinguish benign extracted feature vectors from those of malignant ones by learning a domain invariant space as much as possible. This method achieved the average classification rate of 88.5% on BreaKHis dataset and increased 5.1% classification rate compared with basic methods and 1.25% with state-of-art methods.