Analyzing Intracellular Gradients in Pollen Tubes

Conspicuous intracellular gradients manifest and/or drive intracellular polarity in pollen tubes. However, quantifying these gradients raises multiple technical challenges. Here we present a sensible computational protocol to analyze gradients in growing pollen tubes and to filter nonrepresentative time points. As an example, we use imaging data from pollen tubes expressing a genetically encoded ratiometric Ca²⁺ probe, Yellow CaMeleon 3.6, from which a kymograph is extracted. The tip of the pollen tube is detected with CHUKNORRIS, our previously published methodology, allowing the reconstruction of the intracellular gradient through time. Statistically confounding time points, such as growth arrest where gradients are highly oscillatory, are filtered out and a mean spatial profile is estimated with a local polynomial regression method. Finally, we estimate the gradient slope by the linear portion of the decay in mean fluorescence, offering a quantitative method to detect phenotypes of gradient steepness, location, intensity, and variability. The data manipulation protocol proposed can be achieved in a simple and efficient manner using the statistical programming language R, opening paths to perform high-throughput spatiotemporal phenotyping of intracellular gradients in apically growing cells.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

BACKGROUND AND OBJECTIVE

Peripherally inserted central catheter (PICC) is a novel drug delivery mode which has been widely used in clinical practice. However, long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. Clinically, the postoperative care of PICC is mainly completed by nurses. However, they cannot recognize the correct position of PICC from X-ray chest images as soon as the complications happen, which may lead to improper treatment. Therefore, it is necessary to identify the position of the PICC catheter as soon as these complications occur. Here we proposed a novel multi-task deep learning framework to detect PICC automatically through X-ray images, which could help nurses to solve this problem.

METHODS

We collected 348 X-ray chest images from 326 patients with visible PICC. Then we proposed a multi-task deep learning framework for line segmentation and tip detection of PICC catheters simultaneously. The proposed deep learning model is composed of an extraction structure and three routes, an up-sampling route for segmentation, an RPNs route, and an RoI Pooling route for detection. We further compared the effectiveness of our model with the models previously proposed.

RESULTS

In the catheter segmentation task, 300 X-ray images were utilized for training the model, then 48 images were tested. In the tip detection task, 154 X-ray images were used for retraining and 20 images were used in the test. Our model achieved generally better results among several popular deep learning models previously proposed.

CONCLUSIONS

We proposed a multi-task deep learning model that could segment the catheter and detect the tip of PICC simultaneously from X-ray chest images. This model could help nurses to recognize the correct position of PICC, and therefore, to handle the potential complications properly.

Endotracheal tubes positioning detection in adult portable chest radiography for intensive care unit

PURPOSE

To present an automated method for detecting endotracheal (ET) tubes and marking their tips in portable chest radiography (CXR) for intensive care units (ICUs).

METHODS

In this method, the lung region is first estimated and then the spine is detected between the right lung and the left lung. Because medical tubes are inserted into the body through the throat, the region of interest (ROI) is obtained across the spine. A seed point is determined in the cervical region of the ROI, and then the line path is selected from the seed point. In order to detect ET tubes, the ICU CXR image is preprocessed by contrast-limited adaptive histogram equalization. Then, a feature-based threshold method is applied to the line path to determine the tip location. A comparison to the method by use of Hough transform is also presented. The distance (error) between the detected locations and the locations annotated by a radiologist is used to evaluate the detection precision for the tip location.

RESULTS

The proposed method is evaluated using 44 images with ET tubes and 43 images without ET tubes. The discriminant performance for detecting the existence of ET tubes in this study was 95 %, and the average of detection error for the tip location was approximately 2.5 mm.

CONCLUSIONS

The proposed method could be useful for detecting malpositioned ET tubes in ICU CXRs.