A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Background: In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension. Software: The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density. Conclusions: Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing

Método computacional para segmentação não supervisionada de imagens histológicas de linfoma

Histological image analysis represents a major evolutionary step in modern medicine. Associated with this step, computational methods are being widely developed to help specialists during the analysis of these images to determine diagnostics, prognostics and appropriate treatments in accordance with the condition of the patient. However, when it is performed by specialists, this task becomes time-consuming and susceptible to inter- and intra-pathologist variability. To improve this traditional practice for diagnostics of Mantle Cell Lymphoma, Follicular Lymphoma and Chronic Lymphocytic Leukemia, this study proposes a method for the unsupervised segmentation of nuclear components in indicative cells of such neoplasias using histological images stained with Hematoxylin-Eosin. The proposed method was divided into preprocessing, segmentation and post processing. In the preprocessing step, the techniques used in histogram equalization and Gaussian filter were applied to the channels from RGB color model. In the segmentation, a thresholding technique was applied combining the methods of fuzzy 3-partition entropy and genetic algorithm. Finally, for the improvement of the segmentation results, morphological operations and the valley-emphasis technique were used. For evaluating the developed method, histological images of lymphoma with magnification 20x were selected and manually segmented by a specialist. Those reference images (gold standard) allowed the extraction of quantitative measures in order to compare this method with different techniques proposed in the literature. Furthermore, a qualitative evaluation was conducted leading to relevant and improved results over those from compared studies. Its application was also analysed considering the steps of feature extraction and classification of the lesions, obtaining results of accuracy close to 100%FAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorMestre em Ciência da ComputaçãoA análise de imagens histológicas representa uma das maiores evoluções da medicina moderna. Aliados a essa evolução, métodos computacionais vêm sendo amplamente desenvolvidos para auxiliar especialistas na análise dessas imagens para determinar diagnósticos, prognósticos e tratamentos adequados à condição do paciente. Porém, ao ser realizada por especialistas, essa tarefa torna-se dispendiosa e suscetível a variabilidades inter e intrapatologistas. Para aperfeiçoar tal prática tradicional para diagnósticos de Linfoma de Células do Manto, Linfoma Folicular e Leucemia Linfóide Crônica, este trabalho propõe um método para a segmentação não supervisionada dos componentes nucleares de células indicativas de tais neoplasias utilizando imagens histológicas coradas com Hematoxilina-Eosina. O método proposto foi dividido nas etapas de pré-processamento, segmentação e pós-processamento. Na etapa de pré-processamento, as técnicas de equalização do histograma e filtro gaussiano foram aplicadas sobre os canais componentes do modelo de cores RGB. Na segmentação, foi aplicada uma técnica de limiarização resultante da combinação entre os métodos fuzzy 3-partition entropy e algoritmo genético. Por fim, para aperfeiçoamento dos resultados da segmentação, foram utilizadas operações morfológicas e a técnica valley-emphasis. Para avaliar o método desenvolvido, imagens histológicas de linfoma com magnificação 20x foram selecionadas e segmentadas manualmente por um especialista. Essas imagens de referência (padrão-ouro) permitiram a extração de medidas quantitativas para a comparação entre este método e diferentes técnicas propostas na literatura. Além disso, uma avaliação qualitativa foi realizada levando a resultados relevantes e superiores aos trabalhos comparados. Também foi analisada a sua aplicação sobre as etapas de extração de características e classificação das diferentes lesões consideradas, obtendo resultados de acurácia próximos a 100%