Curvelet initialized level set cell segmentation for touching cells in low contrast images

Detecting pathological brain via ResNet and randomized neural networks

Brain disease is one of the leading causes of death nowadays. Medical imaging is the most effective method for brain disease diagnosis, which provides a clear view of the interior brain. However, manual interpretation requires too much time and effort because medical images contain a large volume of information. Computer aided diagnosis is playing a more and more significant role in the clinic, which can help doctors and physicians to analyze medical images automatically. In this study, a novel pathological brain detection system was proposed for brain magnetic resonance images based on ResNet and randomized neural networks. Firstly, a ResNet was employed as the feature extractor, which was a famous convolutional neural network structure. Then, we used three randomized neural networks, i.e., the Schmidt neural network, the random vector functional-link net, and the extreme learning machine. The weights and biases in the three networks were trained by the chaotic bat algorithm. The three proposed methods achieved similar results based on five runs, and they yielded comparable performance in comparison with state-of-the-art approaches.

Quantitative Determination of Cellular-and Neurite Motility Speed in Dense Cell Cultures

Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.

Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.

Segmentation and classification of two-channel C. elegans nucleus-labeled fluorescence images

BACKGROUND

Aging is characterized by a gradual breakdown of cellular structures. Nuclear abnormality is a hallmark of progeria in human. Analysis of age-dependent nuclear morphological changes in Caenorhabditis elegans is of great value to aging research, and this calls for an automatic image processing method that is suitable for both normal and abnormal structures.

RESULTS

Our image processing method consists of nuclear segmentation, feature extraction and classification. First, taking up the challenges of defining individual nuclei with fuzzy boundaries or in a clump, we developed an accurate nuclear segmentation method using fused two-channel images with seed-based cluster splitting and k-means algorithm, and achieved a high precision against the manual segmentation results. Next, we extracted three groups of nuclear features, among which five features were selected by minimum Redundancy Maximum Relevance (mRMR) for classifiers. After comparing the classification performances of several popular techniques, we identified that Random Forest, which achieved a mean class accuracy (MCA) of 98.69%, was the best classifier for our data set. Lastly, we demonstrated the method with two quantitative analyses of C. elegans nuclei, which led to the discovery of two possible longevity indicators.

CONCLUSIONS

We produced an automatic image processing method for two-channel C. elegans nucleus-labeled fluorescence images. It frees biologists from segmenting and classifying the nuclei manually.