Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Medical Image Segmentation with Deep Learning

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

BACKGROUND: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS: We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways

Automatic Multi-Scale and Multi-Object Pedestrian and Car Detection in Digital Images Based on the Discriminative Generalized Hough Transform and Deep Convolutional Neural Networks

Many approaches have been suggested for automatic pedestrian and car detection to cope with the large variability regarding object size, occlusion, background variability, aspect and so forth. Current state-of-the-art deep learning-based frameworks rely either on a proposal generation mechanism (e.g., "Faster R-CNN") or on the inspection of image quadrants / octants (e.g., "YOLO" or "SSD"), which are then further processed with deep convolutional neural networks (CNN). In this thesis, the Discriminative Generalized Hough Transform (DGHT), which operates on edge images, is analyzed for the application to automatic multi-scale and multi-object pedestrian and car detection in 2D digital images. The analysis motivates to use the DGHT as an efficient proposal generation mechanism, followed by a proposal (bounding box) refinement and proposal acceptance or rejection based on a deep CNN. The impact of the different components of the resulting DGHT object detection pipeline as well as the amount of DGHT training data on the detection performance are analyzed in detail. Due to the low false negative rate and the low number of candidates of the DGHT as well as the high classification accuracy of the CNN, competitive performance to the state-of-the-art in pedestrian and car detection is obtained on the IAIR database with much less generated proposals than other proposal-generating algorithms, being outperformed only by YOLOv2 fine-tuned to IAIR cars. By evaluations on further databases (without retraining or adaptation) the generalization capability of the DGHT object detection pipeline is shown