Machine learning based techniques for biomedical image/video analysis

During the last decade advances in biomedical and information technologies have increased the requirements for objective and automated approaches to analyze large-scale biomedical image data using methods from image processing and computer vision. To explore the data and determine meaningful conclusions, image analysis methods incorporating advanced machine learning algorithms are expected to be beneficial. The primary purpose is to emphasize machine learning potentials for biomedical image processing. It focuses on three particular topics: 1) high-throughput screening (HTS) of a chemical compound library aimed at drug discovery, 2) mitochondria segmentation for morphological subtype quantification, and 3) objective analysis of surgical skills for the capsulorhexis procedure in cataract surgery. We present specific approaches to these problems.;In the first part, our contribution lies in the development of a pipeline algorithm that analyzes microscopic fluorescent cell images in HTS, which screens large chemical libraries for compounds that enhance peroxisome assembly in cells from patients having peroxisome biogenesis disorders (PBDs). The challenge mainly lies in how to accurately detect the peroxisome shown in punctate structures, which indicates the degree of successful peroxisome assembly due to a specific treatment. Ideally, in PBD cells peroxisomes that are completely rescued will present as clearly discernible punctate structures; however, in many cases, cells only respond partially, resulting in a blurry fluorescent textured cytosol background. We successfully overcome this challenge by developing an analysis pipeline. Results will show that our approach is sensitive and can reliably detect recovery of peroxisome assembly in PBD cell lines, and is ready for automated screening of large-scale chemical libraries by applying machine learning while feature extraction and classification are improved.;For the second part, our work explores sub-cellular feature learning to realize fully automated segmentation for mitochondria, which requires more accurate and robust techniques to delineate mitochondria in serial confocal microscopic data. The goal is to establish and validate a data-driven platform for mitochondrial functional analysis based on accurate tracing of mitochondrial structures. Previous solutions are limited due to the inhomogeneity in background intensity, signal-to-noise ratio, and noise level. To address those problems we first use a machine learning approach to estimate the main structures in mitochondria objects and then apply line segment detection to recognize and locate mitochondria centerline fragments. To bridge the fragments, a cost function is developed to judge the occurrence of connection for each pair of centerline fragments. At the output of the segmentation system, standard image segmentation metrics are used to evaluate the results. Our results show that the proposed pipeline achieves segmentation accuracy larger than 98%. In addition to accuracy, another advantage is modularity of the pipeline, as each step in the pipeline could be altered to improve the accuracy for different kinds of image datasets.;The final part of this work explores the use of image analysis methods and computer vision techniques to assess the cataract surgical skills of residents from surgical video data. The work aims at enhancing teaching by accurately measuring and evaluating residents' surgical performance to produce prompt feedback from surgical experts. The capsulorhexis part is studied as it is considered one of the most significant steps in cataract surgery. The ultimate goal is to generate objective numeric assessments for surgical videos. To realize this, a 3-stage video assessment system is developed to process the video frames consistently. The video frame sequence is stabilized and an evaluation metric is developed based on the grading intuition. The registered video frames are then sent to a learning stage so that the surgical instruments in the frames can be identified and extracted. The instrument movement is recognized and tracked. The final stage is to quantify instrument movements such as insertion and withdrawal since these steps may be repeated several times. Experimental results show that our proposed pipeline is able provide a new tool to automatically measure proficiency in capsulorhexis surgery. The results of this work demonstrate that image processing and computer vision approaches coupled with artificial intelligence techniques are valuable for solving several challenging problems of biomedical information processing. The methods produce more objective and reliable results and the variability of inspection techniques involving human observers.