12 citations · 13 across the 2 of their papers we have counts for
6 papers
PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume
Anjali Balagopal, Howard Morgan, Michael Dohopoloski +9
Automatic segmentation of medical images with DL algorithms has proven to be highly successful. With most of these algorithms, inter-observer variation is an acknowledged problem,…
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study
Anjali Balagopal, Dan Nguyen, Maryam Mashayekhi +6
Inter-observer variation is a significant problem in clinical target volume(CTV) segmentation in postoperative settings, where there is no gross tumor present. In this scenario, th…
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
Anjali Balagopal, Dan Nguyen, Howard Morgan +9
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the m…
Reliable Gene Mutation Prediction in Clear Cell Renal Cell Carcinoma through Multi-classifier Multi-objective Radiogenomics Model
Xi Chen, Zhiguo Zhou, Raquibul Hannan +6
Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Because the complete gene mutational landscape cannot be characteri…
Fully Automated Organ Segmentation in Male Pelvic CT Images
Anjali Balagopal, Samaneh Kazemifar, Dan Nguyen +4
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pe…
Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning
Samaneh Kazemifar, Anjali Balagopal, Dan Nguyen +4
Inter-and intra-observer variation in delineating regions of interest (ROIs) occurs because of differences in expertise level and preferences of the radiation oncologists. We evalu…