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Machine Learning for Object Detection and Scene Description in Images and Videos

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Dear Colleagues,

Object detection and scene description are fundamental to advancing computer vision as a tool for automatically understanding the human environment. Recognizing and interpreting objects and scenes is critical for machines to understand and interact with the world meaningfully. This understanding forms the basis for more complex tasks like image and video analysis, autonomous navigation, and interactive systems. These technologies have various applications across various industries, namely healthcare, robotics, automotive, security, etc. Object detection and scene description improve the interaction between humans and computers, making it more intuitive. In big data, these methods enable the analysis and interpretation of visual data, constituting the majority of the data generated today. The complexity of real-world scenes and the variety of objects present ongoing challenges, making this an active and exciting area of research. Improving object detection and scene description models' accuracy, speed, and robustness remains crucial, driving innovation in machine learning algorithms and computational strategies. This Special Issue aims to present recent advances in object detection, semantic and instance segmentation, image captioning, visual question answering, scene modeling, object tracking, video summarizing, action recognition, and all other fields related to machine learning.

Dr. Marcin Iwanowski
Dr. Sungho Kim
Prof. Dr. Zhaoqing Pan
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16 pages, 6525 KiB

Open AccessArticle

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy towards Comparative Performance on Optical Coherence Tomography as on Fundus Autofluorescence

by Zubin Mishra, Ziyuan Chris Wang, Emily Xu, Sophia Xu, Iyad Majid, SriniVas R. Sadda and Zhihong Jewel Hu

Appl. Sci. 2024, 14(17), 7773; https://doi.org/10.3390/app14177773 - 3 Sep 2024

Viewed by 404

Abstract

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique. Full article

(This article belongs to the Special Issue Machine Learning for Object Detection and Scene Description in Images and Videos)

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