--- a
+++ b/dosma/tissues/tibial_cartilage.py
@@ -0,0 +1,334 @@
+"""Analysis for tibial cartilage.
+
+Attributes:
+    BOUNDS (dict): Upper bounds for quantitative values.
+"""
+import os
+import warnings
+
+import numpy as np
+import pandas as pd
+
+from dosma.core.device import get_array_module
+from dosma.core.med_volume import MedicalVolume
+from dosma.core.quant_vals import QuantitativeValueType
+from dosma.defaults import preferences
+from dosma.tissues.tissue import Tissue, largest_cc
+from dosma.utils import geometry_utils, io_utils
+
+import matplotlib.pyplot as plt
+
+# milliseconds
+BOUNDS = {
+    QuantitativeValueType.T2: 60.0,
+    QuantitativeValueType.T1_RHO: 100.0,
+    QuantitativeValueType.T2_STAR: 50.0,
+}
+
+__all__ = ["TibialCartilage"]
+
+
+class TibialCartilage(Tissue):
+    """Handles analysis and visualization for tibial cartilage."""
+
+    ID = 4
+    STR_ID = "tc"
+    FULL_NAME = "tibial cartilage"
+
+    # Expected quantitative values
+    T1_EXPECTED = 1000  # milliseconds
+
+    # Coronal Keys
+    _ANTERIOR_KEY = 0
+    _POSTERIOR_KEY = 1
+    _CENTRAL_KEY = 2
+    _CORONAL_KEYS = [_ANTERIOR_KEY, _CENTRAL_KEY, _POSTERIOR_KEY]
+
+    # Saggital Keys
+    _MEDIAL_KEY = 0
+    _LATERAL_KEY = 1
+    _SAGITTAL_KEYS = [_MEDIAL_KEY, _LATERAL_KEY]
+
+    # Axial Keys
+    _SUPERIOR_KEY = 0
+    _INFERIOR_KEY = 1
+    _TOTAL_AXIAL_KEY = -1
+
+    def __init__(self, weights_dir=None, medial_to_lateral=None):
+        super().__init__(weights_dir=weights_dir, medial_to_lateral=medial_to_lateral)
+
+        self.regions_mask = None
+
+    def unroll_axial(self, quant_map):
+        mask = self.__mask__.volume
+
+        assert (
+            self.regions_mask is not None
+        ), "region_mask not initialized. Should be initialized when mask is set"
+        region_mask_sup_inf = self.regions_mask[..., 0]
+
+        superior = (region_mask_sup_inf == self._SUPERIOR_KEY) * mask * quant_map
+        superior[superior == 0] = np.nan
+        superior = np.nanmean(superior, axis=0)
+
+        inferior = (region_mask_sup_inf == self._INFERIOR_KEY) * mask * quant_map
+        inferior[inferior == 0] = np.nan
+        inferior = np.nanmean(inferior, axis=0)
+
+        total = mask * quant_map
+        total[total == 0] = np.nan
+        total = np.nanmean(total, axis=0)
+
+        return total, superior, inferior
+
+    def split_regions(self, base_map):
+        """Generate subregions for tibial cartilage.
+
+        Tibial cartilage is split into subregions along the 3 major axes:
+        superior/inferior (S/I), anterior/posterior (A/P), medial/lateral (M/L).
+        M/L plateaus are computed with respect to the center of mass (COM)
+        in the sagittal direction. A/C/P divisions are computed independently for each
+        plateau using thirds of the distance between the minimum/maximum pixel in the A/P direction.
+        S/I divisions are computed based on local COM for each 1D column with
+        tissues in the S/I direction.
+
+        The output is stored in `self.regions_mask`.
+
+        Note:
+            Previously, COM was used to split A/P subregions. However, COM is not as robust
+            to split the data into 3 subregions. We use the method described in the reference
+            below: A/C/P are split by thirds of the distance between the minimum/maximum pixel
+            in the A/P direction. Note the minimum/maximum pixel method may not be a robust if
+            there are a sufficient number of erroneous pixels. We do not sufficiently correct
+            for erroneous pixels.
+
+        Args:
+            base_map (ndarray): Binary 3D mask with orientation (SI, AP, ML/LM).
+                If `self.medial_to_lateral`, last dimension should be ML.
+
+        References:
+            Black, MS et al. Detecting Early Changes in ACL-Reconstructed Knees:
+            Cluster Analysis of T2 Relaxation Times from 3 Months to 18 Months Post-Surgery.
+            28th Annual Meeting of ISMRM, Sydney, Australia 2020.
+        """
+        xp = get_array_module(base_map)
+        center_of_mass = geometry_utils.center_of_mass(base_map)  # zero indexed
+        com_med_lat = int(xp.ceil(center_of_mass[2]))
+
+        # M/L
+        region_mask_med_lat = xp.zeros(base_map.shape)
+        region_mask_med_lat[:, :, :com_med_lat] = (
+            self._MEDIAL_KEY if self.medial_to_lateral else self._LATERAL_KEY
+        )
+        region_mask_med_lat[:, :, com_med_lat:] = (
+            self._LATERAL_KEY if self.medial_to_lateral else self._MEDIAL_KEY
+        )
+
+        # S/I
+        locs = base_map.sum(axis=0).nonzero()
+        voxels = base_map[:, locs[0], locs[1]]
+        com_sup_inf = xp.asarray(
+            [
+                int(xp.ceil(geometry_utils.center_of_mass(voxels[:, i])[0]))
+                for i in range(voxels.shape[1])
+            ]
+        )
+        region_mask_sup_inf = xp.full(base_map.shape, self._INFERIOR_KEY)
+        for i in range(len(com_sup_inf)):
+            region_mask_sup_inf[
+                : com_sup_inf[i].item(), locs[0][i].item(), locs[1][i].item()
+            ] = self._SUPERIOR_KEY
+
+        # A/C/P
+        region_mask_ant_post = xp.zeros(base_map.shape)
+        for plateau in [slice(0, com_med_lat), slice(com_med_lat, None)]:
+            cum_ap = xp.nonzero(base_map[..., plateau].sum(axis=(0, 2)))[0]
+            min_ap = xp.min(cum_ap)
+            ap_range = xp.max(cum_ap) - min_ap
+            thresh1, thresh2 = (
+                int(xp.ceil(min_ap + 1 / 3 * ap_range)),
+                int(xp.ceil(min_ap + 2 / 3 * ap_range)),
+            )
+            region_mask_ant_post[:, :thresh1, plateau] = self._ANTERIOR_KEY
+            region_mask_ant_post[:, thresh1:thresh2, plateau] = self._CENTRAL_KEY
+            region_mask_ant_post[:, thresh2:, plateau] = self._POSTERIOR_KEY
+
+        # # A/P
+        # region_mask_ant_post = np.zeros(base_map.shape)
+        # for plateau in [slice(0, com_med_lat), slice(com_med_lat, None)]:
+        #     com = sni.measurements.center_of_mass(base_map[..., plateau])
+        #     com_ant_post = int(np.ceil(com[1]))
+        #     region_mask_ant_post[:, :com_ant_post, plateau] = self._ANTERIOR_KEY
+        #     region_mask_ant_post[:, com_ant_post:, plateau] = self._POSTERIOR_KEY
+
+        self.regions_mask = xp.stack(
+            [region_mask_sup_inf, region_mask_ant_post, region_mask_med_lat], axis=-1
+        )
+
+    def __calc_quant_vals__(self, quant_map, map_type):
+        subject_pid = self.pid
+
+        super().__calc_quant_vals__(quant_map, map_type)
+
+        assert (
+            self.regions_mask is not None
+        ), "region_mask not initialized. Should be initialized when mask is set"
+
+        quant_map_volume = quant_map.volume
+        mask = self.__mask__.volume
+
+        quant_map_volume = mask * quant_map_volume
+
+        axial_region_mask = self.regions_mask[..., 0]
+        sagittal_region_mask = self.regions_mask[..., 1]
+        coronal_region_mask = self.regions_mask[..., 2]
+
+        axial_names = ["superior", "inferior", "total"]
+        coronal_names = ["medial", "lateral"]
+        sagittal_names = ["anterior", "posterior", "central"]
+
+        pd_header = ["Subject", "Location", "Side", "Region", "Mean", "Std", "Median"]
+        pd_list = []
+
+        for axial in [self._SUPERIOR_KEY, self._INFERIOR_KEY, self._TOTAL_AXIAL_KEY]:
+            if axial == self._TOTAL_AXIAL_KEY:
+                axial_map = (axial_region_mask == self._SUPERIOR_KEY).astype(np.float32) + (
+                    axial_region_mask == self._INFERIOR_KEY
+                ).astype(np.float32)
+                axial_map = axial_map.astype(np.bool)
+            else:
+                axial_map = axial_region_mask == axial
+
+            for coronal in [self._MEDIAL_KEY, self._LATERAL_KEY]:
+                for sagittal in [self._ANTERIOR_KEY, self._POSTERIOR_KEY, self._CENTRAL_KEY]:
+                    curr_region_mask = (
+                        quant_map_volume
+                        * (coronal_region_mask == coronal)
+                        * (sagittal_region_mask == sagittal)
+                        * axial_map
+                    )
+                    curr_region_mask = curr_region_mask[curr_region_mask != 0]
+                    # discard all values that are 0
+                    c_mean = np.nanmean(curr_region_mask)
+                    c_std = np.nanstd(curr_region_mask)
+                    c_median = np.nanmedian(curr_region_mask)
+
+                    row_info = [
+                        subject_pid,
+                        axial_names[axial],
+                        coronal_names[coronal],
+                        sagittal_names[sagittal],
+                        c_mean,
+                        c_std,
+                        c_median,
+                    ]
+
+                    pd_list.append(row_info)
+
+        # Generate 2D unrolled matrix
+        total, superior, inferior = self.unroll_axial(quant_map.volume)
+
+        df = pd.DataFrame(pd_list, columns=pd_header)
+        qv_name = map_type.name
+        maps = [
+            {
+                "title": "%s superior" % qv_name,
+                "data": superior,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_superior" % qv_name,
+                "raw_data_filename": "%s_superior.data" % qv_name,
+            },
+            {
+                "title": "%s inferior" % qv_name,
+                "data": inferior,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_inferior" % qv_name,
+                "raw_data_filename": "%s_inferior.data" % qv_name,
+            },
+            {
+                "title": "%s total" % qv_name,
+                "data": total,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_total" % qv_name,
+                "raw_data_filename": "%s_total.data" % qv_name,
+            },
+        ]
+
+        self.__store_quant_vals__(maps, df, map_type)
+
+    def set_mask(self, mask: MedicalVolume, use_largest_ccs=False):
+        xp = get_array_module(mask.A)
+        if use_largest_ccs:
+            msk = xp.asarray(largest_cc(mask.A, num=2), dtype=xp.uint8)
+        else:
+            msk = xp.asarray(mask.A, dtype=xp.uint8)
+        mask_copy = mask._partial_clone(volume=msk)
+        super().set_mask(mask_copy)
+
+        self.split_regions(self.__mask__.volume)
+
+    def __save_quant_data__(self, dirpath):
+        """Save quantitative data and 2D visualizations of tibial cartilage
+        Check which quantitative values (T2, T1rho, etc) are defined for meniscus and analyze these
+        1. Save 2D total, superficial, and deep visualization maps
+        2. Save {'medial', 'lateral'}, {'anterior', 'posterior'},
+            {'superior', 'inferior', 'total'} data to excel file
+
+        :param dirpath: base filepath to save data
+        """
+        q_names = []
+        dfs = []
+
+        for quant_val in QuantitativeValueType:
+            if quant_val.name not in self.quant_vals.keys():
+                continue
+
+            q_names.append(quant_val.name)
+            q_val = self.quant_vals[quant_val.name]
+            dfs.append(q_val[1])
+
+            q_name_dirpath = io_utils.mkdirs(os.path.join(dirpath, quant_val.name.lower()))
+            for q_map_data in q_val[0]:
+                filepath = os.path.join(q_name_dirpath, q_map_data["filename"])
+                xlabel = "Slice"
+                ylabel = ""
+                title = q_map_data["title"]
+                data_map = q_map_data["data"]
+
+                plt.clf()
+
+                upper_bound = BOUNDS[quant_val]
+                if preferences.visualization_use_vmax:
+                    # Hard bounds - clipping
+                    plt.imshow(data_map, cmap="jet", vmin=0.0, vmax=BOUNDS[quant_val])
+                else:
+                    # Try to use a soft bounds
+                    if np.sum(data_map <= upper_bound) == 0:
+                        plt.imshow(data_map, cmap="jet", vmin=0.0, vmax=BOUNDS[quant_val])
+                    else:
+                        warnings.warn(
+                            "%s: Pixel value exceeded upper bound (%0.1f). Using normalized scale."
+                            % (quant_val.name, upper_bound)
+                        )
+                        plt.imshow(data_map, cmap="jet")
+
+                plt.xlabel(xlabel)
+                plt.ylabel(ylabel)
+                plt.title(title)
+                clb = plt.colorbar()
+                clb.ax.set_title("(ms)")
+                plt.axis("tight")
+
+                plt.savefig(filepath)
+
+                # Save data
+                raw_data_filepath = os.path.join(
+                    q_name_dirpath, "raw_data", q_map_data["raw_data_filename"]
+                )
+                io_utils.save_pik(raw_data_filepath, data_map)
+
+        if len(dfs) > 0:
+            io_utils.save_tables(os.path.join(dirpath, "data.xlsx"), dfs, q_names)