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Automatic Segmentation of the Prostate On CT Images Using a Bi-Directional Convolutional LSTM U-Net with Novel Loss Function

X Li1*, H Bagher-Ebadian2, C Li1, E Mohamed2, F Siddiqui2, B Movsas2, D Zhu1, I Chetty2, (1) Wayne State University (2) Henry Ford Health System, Detroit, MI


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Deep learning-based networks have achieved practical performance in the field of medical image segmentation. Here we introduce a deep neural network with an enhanced U-net architecture trained using a novel loss function to perform automatic segmentation of the prostate gland on pre-treatment planning CT images of patients with prostate cancer.

Methods: Planning-CT datasets for 100 prostate cancer patients were retrospectively evaluated as part of an IRB-approved protocol. Physician-delineated images constituted the ground-truth. All images were linearly interpolated/resampled to a spatial resolution of 1×1×1.5mm. Image regions containing the cross-section of the prostate gland (128×128mm2) were used for training. Patient dataset was split into cohorts of train (55%), validation(15%), and test (30%) using a bi-directional convolutional long short-term memory (ConvLSTM) U-Net with densely connected convolutions (BCDU-Net).Two region-based loss functions (Focal Tversky Loss, FTL and Dice Loss, DL) were weighted combined to minimize the mismatch and maximize the overlapping regions between the ground-truth and the predicted segmentation. The FTL function is especially useful in dealing with extreme foreground-background class imbalance, as is the case with segmentation of the prostate gland vs. the surrounding background.

Results: Results comparing the optimal BCDU-Net (FTL+DL) and physician-generated contours for the test data were: DSC (Dice) =0.84, Area-under-receiver-operator-characteristic-curve (AUC=0.98), Area-under-precision-recall-curve (AUPRC=0.98), JSC (Jaccard) =0.98, Sensitivity=0.82, Specificity=0.99, Accuracy=0.99, and Precision=0.85. Results showed improved performance when the combined loss function vs. dice-based loss function only were used for training the deep neural network (see supporting doc).

Conclusion: Despite the small sample size BCDU-Net-based model utilizing a weighted loss function combining dice and focal tversky losses showed promise for automatic segmentation of the prostate gland on planning CT image datasets. Further investigation is warranted.

Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by a grant from Varian Medical Systems (Palo Alto, CA)


Segmentation, CT, Prostate Therapy


IM/TH- image Segmentation: CT

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