Inter and Intra Slice Reduction in Brain Tumor Segmentation
Bibliographic record
Abstract
Medical Image Segmentation is a process of segmenting abnormalities from normal tissues.Due to the increasing growth of deep learning, there are various deep network models used to segment 3D medical images.Recently, U-Net and V-Net are used to segment 3D medical images.But these networks suffer from high computation burden.The objective of this paper is minimizing the computation time by reducing the input data.Initially, the 3D slices are reduced by taking the average of few slices (Inter-slice reduction).Then, only the tumor area is segmented using detection window (Intra-slice reduction).The reduced 3D Magnetic Resonance Imaging (MRI) data was fed as input to UNet with Long Short Term Memory (LSTM) layers for segmentation and classification.BRATS 2017 and BRATS 2018 are tested by proposed method of dataset.It achieves 96.24% accuracy, 90.84% Dice Score Coefficient (DSC) on BRATS 2017 dataset and 92% accuracy and 88.88% DSC on BRATS 2018 dataset in 12 and 10 seconds respectively.The proposed method is compared with some recent methods.It achieved reasonable gain in computation time with negligible loss in other metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".