phase2.pptx project slides which helps to know the content

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1. PES College of Engineering Under the Guidance of: Mrs. Ramyashree H P Assistant Professor Department of CSE Department of Computer Science and Engineering PROJECT PRESENTATION ON Multi-organ Nuclei Segmentation and Classification Presented By: Mohanapriya K.J [4PS20CS059] Nanditha KP [4PS20CS062] Thejaswini Raj M D [4PS20CS117] Vishwas B C [4PS20CS124]

2. Data Preprocessing Primary objective of this project is to develop a generalized model capable of accurately segmenting nuclei in whole slide images from various organs, addressing the limitations of current organ-specific methods in cancer diagnostics Data Preprocessing: To implement effective preprocessing techniques on hematoxylin and eosin stained images in our multi-organ dataset to enhance the quality and consistency of the images for improved accuracy in nuclei segmentation and classification. The preprocessing steps involve dividing the whole slide images into patches, discarding certain patches based on the mean value of their masks, and splitting the dataset into training and validation sets. The dataset is split into training and validation sets using an 80:20 ratio.

3. Data Augmentation Data Augmentation: To employ data augmentation techniques, such as random rotations, flips, and adjustments in brightness and contrast, during the training phase of our CNN model. This approach aims to expand the dataset for multi-organ nuclei segmentation and classification, compensating for the limited size of the original dataset. Data augmentation is typically used during the training phase to artificially increase the diversity of the training dataset, thereby improving the generalization and robustness of the model. Rotation : Randomly rotating the image by a certain angle. Zoom : Randomly zooming into or out of the image. Brightness and Contrast : Adjusting the brightness and contrast of the image.  Horizontally Flipped and also Rotated By 80,90,270 degree.

4. Segmentation and Classification Segmentation : To implement Convolutional Neural Networks (CNN) for the precise segmentation of individual cells in whole slide images across multiple organs. This step is crucial as it facilitates the detailed analysis of cell distribution and morphology within the tissue. Segmentation is performed to identify and delineate the boundaries of nuclei within the images. This is essential for subsequent classification tasks, where the type of cells present in each segmented nucleus is determined (e.g., epithelial cells, lymphocytes, macrophages, and neutrophils). Classification : This classification process is aimed at providing a deeper insight into cellular structures, aiding in precise disease diagnosis, and informing the development of targeted treatment strategies. The success of this objective will significantly contribute to advancing the field of cancer diagnostics and therapy

5. PatchEUNet PatchEUNet is a convolutional neural network architecture designed for semantic segmentation tasks, particularly in medical image analysis. It combines elements of U-Net architecture with patch-based processing for efficient context capture and precise localization. This architecture is specifically designed to tackle the challenges of segmenting high-resolution medical images by using a sophisticated encoder-decoder structure, incorporating the EfficientNet-B3 architecture, and leveraging skip connections.

6. Encoder Contracting Path (Encoder) : EfficientNet-B3 Integration : The encoder in PatchEUNet is based on the EfficientNet-B3 architecture, which is known for its efficiency and scalability. EfficientNet-B3 is used to extract features from the input whole slide images (WSIs) in a hierarchical manner. Hierarchical Downsampling : The encoder progressively reduces the spatial dimensions of the input images while increasing the depth of the feature maps. This downsampling process captures high-level contextual information at multiple scales, which is crucial for segmenting nuclei across different organs and staining variations. Pre-trained Weights : The encoder is initialized with weights from the ImageNet dataset, allowing the model to leverage pre-learned features. This pre-training step enhances the model's ability to recognize diverse patterns and textures in the WSIs, which is essential for accurate segmentation and classification. Overall, the encoder in PatchEUNet extracts high-level features from the input WSIs using the EfficientNet-B3 architecture,

7. Decoder Expanding Path (Decoder) : Upsampling and Convolutional Layers : The decoder in PatchEUNet reverses the downsampling process of the encoder by progressively upsampling the feature maps and applying convolutional layers. This upsampling process helps to recover spatial details lost during downsampling and refine the segmentation masks. Every step in the expansive path consists of an upsampling of the feature map, followed by a convolution. Hence, the expansive branch increases the resolution of the output. In order to localize the upsampled features, the expansive path combines them with high-resolution features from the contracting path via skip-connections [3]. The output of the model is a pixelby -pixel mask that shows the class of each pixel Batch Normalization and ReLU Activation : Each convolutional layer in the decoder is followed by batch normalization and ReLU activation. Batch normalization standardizes the activations from a layer, which helps in stabilizing and accelerating the training process. ReLU activation introduces non-linearity, enabling the model to learn complex mappings from the feature maps to the segmentation masks. Skip Connections : Skip connections are used to concatenate feature maps from the encoder (EfficientNet-B3 blocks) to the corresponding decoder layers. This mechanism helps in recovering spatial details and combining high-resolution features from the encoder with the upsampled features in the decoder, improving the accuracy of segmentation. We use 256, 128, 64, 32 and 16 filters for the convolutional layers in the decoder blocks. Each decoder block is combined to output of the EfficientNet-B3 blocks numbered 2, 3, 4 and 6 via skip-connections. The decoder reconstructs the segmented masks by progressively upsampling the features and refining the segmentation details.

8. Efficient Net b3 EfficientNet-B3 architecture [4], which results from a compound scaling method applied on the baseline network EfficientNet-B0 that uniformly scales all three dimensions with a fixed ratio. EfficientNet-B3 specifically refers to a particular configuration of the EfficientNet architecture, where the depth, width, and resolution of the blocks are optimized to balance between accuracy and efficiency. It's characterized by deeper and wider layers compared to smaller variants like EfficientNet-B0, but not as heavy as larger variants like EfficientNet-B7. The encoder component of PatchEUNet is based on the EfficientNet-B3 architecture, which is known for its efficiency and effectiveness in image classification tasks. It consists of seven blocks that hierarchically downsample the input image while preserving important features. Each block contains a sequence of layers including convolutional layers, activation functions, and other operations Each block incorporates multiple operations to efficiently process the input data and extract relevant features. By utilizing EfficientNet-B3 as the encoder, PatchEUNet benefits from its ability to capture rich contextual information from the input whole slide images.