Kidney and Kidney Tumor Segmentation, 2019 (KiTS-19) / (Record no. 608904)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02332nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Abbasi, Ramsha |
| 245 ## - TITLE STATEMENT | |
| Title | Kidney and Kidney Tumor Segmentation, 2019 (KiTS-19) / |
| Statement of responsibility, etc. | Ramsha Abbasi |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 58p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Computed Tomography (CT) is the most widely used imaging procedure for locating<br/>and diagnosing kidney tumors. The standard treatment for kidney tumors is surgical<br/>removal. It is important to accurately segment the kidney and its tumor for effective<br/>surgical planning. The manual segmentation of kidney tumors is time-consuming and<br/>subject to variability between different radiologists. Therefore, automatic semantic<br/>segmentation of kidney tumors using deep learning networks has become increasingly<br/>popular in the past few years. Automatic segmentation of kidney tumors is a very<br/>challenging task due to their morphological heterogenicity. This work provides the<br/>application of 3D UNet and 3D SegResNet on KiTS19 challenge data for accurate<br/>segmentation of kidney and kidney tumors. An ensembling operation was added in the<br/>end to average the predictions of all models. The proposed method is based on the<br/>MONAI framework and focuses more on training procedure rather than complex<br/>architectural modifications. The models were trained using KiTS19 training set of 210<br/>cases for which ground truth labels were available. The training data was divided into<br/>190:20, for training and validation respectively. We evaluated the performance of our<br/>network on KiTS19 official test set and obtained mean dice of 0.8964, 0.9724 kidney<br/>dice, and 0.8204. Our approach outperforms many submissions in terms of kidney<br/>segmentation and gives promising results for tumor segmentation. We also used a local<br/>test set of 90 cases from KiTS21 challenge to check how well our method adopts to a<br/>new dataset. It scored a mean dice of 0.9160, kidney dice of 0.9771, and 0.8550 tumor<br/>dice. The obtained results on KiTS19 official test set and local test set show that our<br/>approach is effective and can be used for organ segmentation. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/31041">http://10.250.8.41:8080/xmlui/handle/123456789/31041</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 04/22/2024 | 610 | SMME-TH-773 | Thesis |