Microtubule Detection and Instance Segmentation using Deep Convolutional Neural Networks

Microtubules are long cylindrical polymers, assembled from tubulin proteins. Microtubule ends can be visualized using fluorescence and confocal microscopy. This allows for the study of microtubule dynamics. However, the manual annotation of microtubules is laborious, which is why automated tracking methods are used. In this project we have...

Interactive semantic segmentation of 3D medical images: Comparative analysis of discrete and gradient descent based batch query retrieval methods in active learning

Accurate segmentation of anatomical structures and abnormalities in medical images is crucial, but manual segmentation is time-consuming and automated approaches lack clinical accuracy. In recent years, active learning approaches that aim to combine automatic segmentation with manual input have gained attention in the field, aiming to reduce the...

Optimization Algorithms for Plane Selection in Interactive 3D Image Segmentation

Segmentation of 3D medical images is useful for various medical tasks. However, fully automated segmentation lacks the accuracy required for medical purposes while manual segmentation is too time-consuming. Therefore, an active learning method can be used to generate an accurate segmentation using less user input. The active learning pipeline...

Improving the Random Walker algorithm for interactive 3D medical image segmentation using AI predictions: Modify the weight function to rely on an ensemble of segmentation predictions

The segmentation of anatomical structures in 3D medical images is crucial for various applications in the field of medical imaging. Fully automated methods often lack accuracy, while manual segmenting requires much time and effort from a user. Due to this, Active Learning approaches are being proposed to solve this by making the method...

Effect of different uncertainties in medical image segment error estimation: Interactive segmentation of 3D medical images

Although automated segmentation of 3D medical images produce near-ideal results, they encounter limitations and occasional errors, necessitating manual intervention for error correction. Recent studies introduce an active learning pipeline as an efficient solution for this, requiring user corrections only on some of the most uncertain parts of...

Inferring Segments of Speaking Intention Using a Body-worn Accelerometer: Enhancing social interaction with AI-powered systems

This research paper proposes a deep learning model to infer segments of speaking intentions using body language captured by a body-worn accelerometer. The objective of the study is to detect instances where individuals exhibit a desire to speak based on their body language cues. The labeling scheme employed is a binary string, with “0”...

Image Analysis for All-Optical Electrophysiology

The study of the electrophysiological properties of neurons has reached a new level thanks to recent techniques that combine knowledge from different fields of science. For a method such as all-optical electrophysiology, the quality of cell segmentation in the image has one of the critical roles since the accuracy of illumination and...

EV-Mask-RCNN: Instance Segmentation in Event-based Videos

Instance segmentation on data from Dynamic Vision Sensors (DVS) is an important computer vision task that needs to be tackled in order to push the research forward on these types of inputs. This paper aims to show that deep learning based techniques can be used to solve the task of instance segmentation on DVS data. A high performing model was...

Detecting strawberries using different Convolutional Neural Networks

This paper tries to combat the food waste of strawberries during the harvesting steps.<br/>An automatic pipeline must be established to combat this food waste.<br/>One of the steps needed in this pipeline is detecting strawberries in images.<br/>Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to...

Color Invariant Convolution for semantic segmentation

Color Invariant Convolution (CIConv) is a learnable Convolutional Neural Network (CNN) layer that reduces the distribution shift between the source and target set in the CNN under an illumination-based domain shift. We explore the semantic segmentation performance for daynight domain adaptation when using CIConv. We will test this on two...

Transferring a Segmentation Task Between Real and Synthetic Data

In the field of ecology, camera traps are important tools to collect information on the wildlife of certain areas. The problem that arises with many camera traps is that they can collect more images than a human can realistically go trough all by themselves. To help classify these images computer vision is proposed as an alternative to manual...

Customer segmentation using RFM analysis

This paper is a research on the segmentation of customers. The clustering of customers is done based on the variables recency, frequency and monetary value. Such a clustering is called an RFM-model. The clustering is done using the K-means clustering method. To find the optimal number of clusters the following performance metrics are used: Elbow...

An evaluation of image segmentation techniques for MRI scans

In this thesis, several image segmentation techniques will be tested that eventually will be applied to MRI brain scans in order to detect hydrocephalus. The meth- ods include Sobel edge detection, Canny edge detection, active contour model (also known as snakes), k-means clustering and region growing. Furthermore two exten- sions are discussed....

B2B Customer Insight Tool: Automated Data Analytics to improve the Deal Analytics workflow

The Deal Analytics group of PricewaterhouseCoopers Amsterdam has requested a tool for automatising the business-to-business customer analysis. This analysis was performed manually, which left room for performance improvement. This report discusses how the a product was developed which automates the analyses After two weeks of initial research, a...

Analysis of Microscopic Images: Boundary Detection Using a Modified Mumford-Shah Model

Deformable energy-minimizing contours are used in image analysis, particularly to detect boundaries from objects. From the Mumford-Shah model we derive an equation to move such contours towards the boundary, which minimizes the functional. The external force is computed using the mean value of the inside and outside area of the contour. Various...

Facade labelling using neural networks

This report describes the process of the Bachelorproject(TI3806) done for ‘De Energiebespaarders’, a startup in Amsterdam striving to make homes more energy efficient through accessible advice and installation of insulation or solar panels. The goal of the project was to apply machine learning to improve their system for identifying house...