3D semantic segmentation of brain tumour for overall survival prediction

#Image #Processing #Brain #Tumor #Segmentation #Survival #Prediction

A 3D FCN based Brain Tumor Segmentation for Overall Survival Prediction, is a research based project presented by Dr. Rupal R. Agravat and Dr. Mehul S. Raval. This model basically deals with the segmentation of brain tumors at an early stage, due to which the diagnosis of the same can lead to a proper treatment. Out of all types of brain tumors, Glioma is one of the most life-threatening brain tumors. The brain tumor treatment planning is highly dependent on the accurate tumor sub-component segmentation, but due to heterogeneous nature of Glioma the segmentation task becomes difficult. The segmentation results fed into RFR to predict the overall survival of the patients. The RFR was trained on the age, shape and volumetric features extracted from the ground truth provided with the training dataset. The dataset contains all the images that has been segmented manually, by one to four raters. Features like age, survival days, and resection status for 237 HGG scans are provided separately for Overall Survival (OS).
To sum up the above process, there is a proposed method which contains majorly two tasks:
Tumor Segmentation and Survival Prediction.
Further, there are the implementation details as pre-processing, training and post-processing. Pre-processing boosts network training and improves the performance. All four modality images are bias field corrected. The network is trained on entire training image dataset. The network uses the combination of two different loss functions namely, dice loss function and focal loss function. The prediction of single image takes around one minute. Finally, post-processing includes conversion of the enhancing tumor into necrotic. Later, the parameters like sensitivity and specificity are recorded in two datasets; Training dataset and Validation Dataset.
In the conclusion, the proposal basically uses three-layer deep U-net based encoder-decoder architecture for the segmentation. Each layer of the encoding side incorporates dense modules and decoding side convolution modules. Later, network segmentation for cases with GTR tests RFR for overall survival prediction.

• Date: 12 Sep 2020
• Time: 10:00 AM to 12:45 PM
• All times are (UTC+05:30) Chennai
• Add Event to Calendar
  iCal
  Google Calendar

If you are not a robot, please complete the ReCAPTCHA to display virtual attendance info.

• Ahmedabad, Gujarat
• India

---

• Contact Event Host

• Starts 07 September 2020 04:00 PM
• Ends 11 September 2020 09:30 PM
• All times are (UTC+05:30) Chennai
• No Admission Charge

•