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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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4 results for “organ-at-risk”

A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planningcs.CV

New method segments brain tumors and organs-at-risk for radiation therapy.

problem Automating segmentation of brain tumors and organs-at-risk in radiation therapy planning.

method Combines contrast-adaptive generative model and spatial regularization model.

result Tumor segmentation accuracy comparable to state-of-the-art methods.

brain tumors radiation therapy organ-at-risk contrast-adaptive spatial regularization

2018-07-18abs ↗pdf ↗

Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planningcs.LG

CNNs can be trained with clinically available segmentations for OARs in radiotherapy.

problem Lack of dedicated training volumes for CNNs in radiotherapy.

method Used clinically available segmentations from PACS, applied multi-label segmentation, empirically assessed training set size.

result Clinically available segmentations can be used to train an accurate OAR segmentation model.

CNNs radiotherapy segmentation PACS multi-label

2019-11-12abs ↗pdf ↗

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapycs.CV

Deep learning improves radiotherapy by automating head and neck cancer organ segmentation.

problem Manual delineation of radio-sensitive organs in head and neck cancer radiotherapy is time-consuming and variably performed.

method A 3D U-Net deep learning architecture trained on clinical CT scans and expert segmentations.

result Deep learning model achieves expert-level performance in delineating 21 distinct OARs.

deep learning radiotherapy head and neck cancer organ segmentation surface Dice similarity coefficient

2018-09-12abs ↗pdf ↗

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapyphysics.med-ph

A ML-based method reconstructs 3D organ doses from 2D radiographs for pediatric abdominal radiotherapy.

problem Reconstructing detailed 3D dose distributions for childhood cancer survivors using limited 2D radiographs.

method Surrogate-free ML approach using 142 abdominal planning CTs, 300 artificial plans, and evolutionary algorithm.

result Accurate 3D dose reconstructions with MAEs ≤ 1.7 Gy for edge organs, validated on independent dataset.

ML radiotherapy organ dose pediatric abdominal

2020-02-17abs ↗pdf ↗