| dc.description.abstract | Clinical pathology is one of the finest diagnostic techniques for all types of cancer, and
its outcome determines the treatment plan for a patient. In this research, two types of
cancers are taken into account, Osteosarcoma and Renal Cell Carcinoma, where one
of the major prognostic factors is the amount of tumor necrosis created due to Neo-
adjuvant chemotherapy. Osteosarcoma is a high-grade malignant bone tumor and Re-
nal Cell Carcinoma is the most common type of Kidney Cancer. Tumor necrosis is
a condition where the tumorous tissues cannot perform their normal metabolic func-
tions and gradually die. Neo-adjuvant chemotherapy is the treatment given to a pa-
tient’s body before starting the main treatment. The proposed study aims to develop
an automated tool for quantitative image analysis of digital histopathology images of
post-neoadjuvant resection specimens. Post-neoadjuvant chemotherapy resection spec-
imens refer to the tissue samples collected from cancer patients who have undergone
Neo-adjuvant chemotherapy. Even though so many tools are available for cancer di-
agnosis, there is no specific tool to calculate the percentage of necrosis available in
post-neoadjuvant chemotherapy resection specimens of Osteosarcoma and Renal Cell
Carcinoma.
Necrosis-ML is an algorithm that has been developed in this work to perform image-
level segmentation and quantization. The two major tasks involved in this tool are the
semantic segmentation and quantization of segmented masks. In this algorithm, the in-
put image will undergo a patchification process and segmentation will be performed
at patch-level. The segmentation model is set up with U-Net++ architecture using
ResNet101 as the feature extractor. The mask of each segmented patch will be merged
to get a single binary mask and it will be quantified to get the final result. Pathologists
can feed the histopathology images captured from the digital microscope into this tool
and the output will be the segmented image and the total area of this segmented part.
Different other methods have been proposed in this research for the segmentation
task as this is the only AI-enabled part of this study. One among them is a segmen-
tation model using Autoencoder where the training of the model is done with a single
histopathology image. Another proposed model is designed using SegFormer, which is
a transformer-based, encoder-decoder architecture. The SegFormer model also can be
used to develop Necrosis-ML by replacing the U-Net architecture mentioned above.
Another major contribution of this research is the dataset created in this study,
named as NecrosisDB. This dataset contains 900 patches of images that include var-
ious morphologies of necrosis, tumors, fibrosis, and other frequently occurring tissue
elements. The major hurdle in any research in the medical domain is the unavailability
of the annotated datasets, but in this study, we managed to create a fully supervised
annotated dataset that has been annotated using experienced pathologists. This dataset
has been made publicly accessible for research or study purpose based on some terms
and conditions. It can be accessed from the link, | en_US |
