Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
- Title
- Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
- Creator
- Patil A.P.; Hiremath M.
- Description
- Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE.
- Source
- 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1080-1084.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep learning; Lymphocyte; Segment Anything Model; Segmentation; white blood cells
- Coverage
- Patil A.P., Christ (Deemed to Be University), Department of Computer Science, India, Cmr Institute of Technology, Department of Computer Application, Bengaluru, India; Hiremath M., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038459-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Patil A.P.; Hiremath M., “Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19051.