ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study
Liang Qiu, Liyue Shen, Lianli Liu, Junyan Liu, Yizheng Chen, and Lei, Xing

TL;DR
This paper introduces ST-NeRP, a neural framework that models patient-specific spatial-temporal changes in medical images, enabling better monitoring of disease progression and treatment response over time.
Contribution
It develops a novel spatial-temporal neural representation method using implicit neural networks and prior embeddings for patient-specific imaging analysis.
Findings
Effective in modeling 4D CT and longitudinal CT sequences
Demonstrates accurate prediction of anatomical deformations over time
Shows potential for improved disease monitoring in clinical settings
Abstract
During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturing and predicting the spatial-temporal anatomic changes from a sequence of patient-specific image series presents a considerable challenge. Thus, the development of a computational framework becomes highly desirable for a multitude of practical applications. In this context, we propose a strategy of Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP) for patient-specific imaging study. Our strategy involves leveraging an Implicit Neural Representation (INR) network to encode the image at the reference time point into a prior embedding. Subsequently, a spatial-temporally continuous deformation function is learned through another INR network. This network is trained using the whole…
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Taxonomy
TopicsMedical Imaging and Analysis · AI in cancer detection · Radiomics and Machine Learning in Medical Imaging
