Regression Concept Vectors for Bidirectional Explanations in Histopathology

Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making. In this work, we propose a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer. The directional derivative of the decision function along the RCVs represents the network sensitivity to increasing values of a given concept measure. When applied to breast cancer grading, nuclei texture emerges as a relevant concept in the detection of tumor tissue in breast lymph node samples. We evaluate score robustness and consistency by statistical analysis.Comment: 9 pages, 3 figures, 3 table

A new procedure for lung region segmentation from computed tomography images

Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method

An overview of medical image processing methods

Since human life is worthier than all things, efforts on virtual animation and visualization of human body’s viscera, without surgical interference to diagnose a disease is very important. Recently, modern medical instruments are able to produce views which can be used for better diagnoses and accurate treatment. Various standards were formed regarding these instruments and end products that are being used more frequently everyday. Personal computers (PCs) have reached a significant level in image processing, carried analysis and visualization processes which could be done with expensive hardware on doctors’ desktops. The next step is to try to find out proper solutions by software developers andengineers that help doctors to make decision by combining opportunities in these two scientific areas. The objective of the present study is to construct 3D models and present it to users on screen in personal computers by using data acquired from tomography and magnetic resonance instruments. In order to realize this objective, developing software is aimed. In the second and third sections, the datastructures and processing of 3D volumetric data in digital format, 3D visualization techniques and theoretical subjects about methods and algorithms used are explained. In the forth section, explanations on developing a software package for the realization of the objective of the study, its usage and information about software development tools used are given. In the last section, the determinations made at the end of trials in this study, difficulties met and recommendations obtained in the light of the trial results are presented

Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model

A framework for quantification and physical modeling of cell mixing applied to oscillator synchronization in vertebrate somitogenesis

In development and disease, cells move as they exchange signals. One example is found in vertebrate development, during which the timing of segment formation is set by a ‘segmentation clock’, in which oscillating gene expression is synchronized across a population of cells by Delta-Notch signaling. Delta-Notch signaling requires local cell-cell contact, but in the zebrafish embryonic tailbud, oscillating cells move rapidly, exchanging neighbors. Previous theoretical studies proposed that this relative movement or cell mixing might alter signaling and thereby enhance synchronization. However, it remains unclear whether the mixing timescale in the tissue is in the right range for this effect, because a framework to reliably measure the mixing timescale and compare it with signaling timescale is lacking. Here, we develop such a framework using a quantitative description of cell mixing without the need for an external reference frame and constructing a physical model of cell movement based on the data. Numerical simulations show that mixing with experimentally observed statistics enhances synchronization of coupled phase oscillators, suggesting that mixing in the tailbud is fast enough to affect the coherence of rhythmic gene expression. Our approach will find general application in analyzing the relative movements of communicating cells during development and disease.Fil: Uriu, Koichiro. Kanazawa University; JapónFil: Bhavna, Rajasekaran. Max Planck Institute of Molecular Cell Biology and Genetics; Alemania. Max Planck Institute for the Physics of Complex Systems; AlemaniaFil: Oates, Andrew C.. Francis Crick Institute; Reino Unido. University College London; Reino UnidoFil: Morelli, Luis Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Max Planck Institute for Molecular Physiology; Alemania. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin

Deep Learning on Wound Segmentation and Classification: A Short Review and Evaluation of Methods Used

The abundance of research on wound segmentation suggests that it is significant in order to provide a good analysis and assistance in the medical field. Although there is some relative dearth of wound segmentation on other approaches, this review finds that deep learning is central to the objective of image segmentation. Here, the review informs on the methods that are credible towards wound segmentation, training, classification, validation of datasets, data collection, and evaluation of segmented images. While the literature establishes a clear connection between the segmentation algorithms of the object, therefore this study seeks to find the segmentation algorithm directly applicable to wound assessment